Normalized Discounted Cumulative Gain Research Articles

Inconsistency among evaluation metrics in link prediction

Abstract Link prediction is a paradigmatic and challenging problem in network science, which aims to predict missing links, future links and temporal links based on known topology. Along with the increasing number of link prediction algorithms, a critical yet previously ignored risk is that the evaluation metrics for algorithm performance are usually chosen at will. This paper implements extensive experiments on hundreds of real networks and 26 well-known algorithms, revealing significant inconsistency among evaluation metrics, namely different metrics probably produce remarkably different rankings of algorithms. Therefore, we conclude that any single metric cannot comprehensively or credibly evaluate algorithm performance. In terms of information content, we suggest the usage of at least two metrics: one is the area under the receiver operating characteristic curve (AUC), and the other is one of the following three candidates, say the area under the precision-recall curve (AUPR), the area under the precision curve (AUC-Precision), and the normalized discounted cumulative gain (NDCG). When the data is imbalanced, say the number of negative samples significantly outweighs the number of positive samples, the area under the generalized ROC curve (AUC-gROC) should also be used. In addition, as we have proved the essential equivalence of threshold-dependent metrics, if in a link prediction task, some specific thresholds are meaningful, we can consider any one threshold-dependent metric with those thresholds. This work completes a missing part in the landscape of link prediction, and provides a starting point toward a well-accepted criterion or standard to select proper evaluation metrics for link prediction.

AI-Powered Search Systems : Integrating Machine Learning with Search Technology for High-Scalability Applications

This article comprehensively analyzes integrating Artificial Intelligence (AI) and machine learning techniques into high-scalability search systems. We explore AI-powered search's theoretical foundations and practical implementations, focusing on advanced ranking algorithms, natural language processing for query understanding, and optimized distributed architectures. We demonstrate significant improvements in search relevance and efficiency through experiments conducted on a large-scale dataset comprising 100 million web pages and 1 million real-world queries. Our AI-powered system showed a 15% increase in Normalized Discounted Cumulative Gain (NDCG) for complex queries and a 12% improvement in Mean Reciprocal Rank (MRR) for navigational queries compared to traditional keyword-based approaches. We also address critical challenges in maintaining system scalability and performance, including data synchronization, real-time model updates, and resource management in distributed environments. The article further discusses emerging trends, such as graph neural networks and multimodal search capabilities, alongside ethical considerations and data privacy concerns. Our findings provide valuable insights for researchers and practitioners aiming to develop next-generation search platforms capable of handling the increasing complexity and volume of digital information while ensuring responsible AI integration.

Heterogeneous Graph Neural Network with Hierarchical Attention for Group-Aware Paper Recommendation in Scientific Social Networks

In recent years, the academic groups established in Scientific Social Networks (SSNs) have not only facilitated collaboration among researchers but also enriched the relations in SSNs, providing valuable information for paper recommendation tasks. However, existing paper recommendation methods rarely consider group information and they fail to fully leverage the group information due to the heterogeneous and complex relations between researchers, papers, and groups. In this paper, a heterogeneous graph neural network with hierarchical attention, named HHA-GPR, is proposed for group-aware paper recommendation. Firstly, a heterogeneous graph is constructed based on the interactions of researchers, papers, and groups in SSNs. Secondly, a random walk-based sampling strategy is utilized to sample highly correlated heterogeneous neighbors for researchers and papers. Thirdly, a hierarchical attention network with intra-type and inter-type attention mechanisms is designed to aggregate the sampled neighbors and comprehensively model the complex relations among the heterogeneous neighbors. More specifically, an intra-type attention mechanism is introduced to aggregate the neighbors of the same type, and an inter-type attention mechanism is employed to combine the embeddings of different types to form the ultimate node embedding. Extensive experiments are conducted on the real-world CiteULike and AMiner datasets, and the experimental results demonstrate that our proposed method outperforms other benchmark methods with an average improvement of 5.3% in Precision, 5.6% in Recall, and 5.1% in Normalized Discounted Cumulative Gain (NDCG) across both datasets.

Sign language recognition using modified deep learning network and hybrid optimization: a hybrid optimizer (HO) based optimized CNNSa-LSTM approach.

Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

A Study on Doctor Recommendation Model in Medical Guidance Services

In medical guidance services, it is of great significance to match the appropriate department and doctor by digging deeper into patients’ demands. However, accurate matching of doctors requires the ability to locate the exact department based on the text of the patients’ chief complaint, and then select the matching doctor by considering the patients’ condition, the doctor’s professionalism, and the patients’ preference. To this end, this paper proposes a department classification model on the basis of Convolutional Neural Networks (CNN) as well as Robustly optimized BERT approach (RoBERTa) with an attention mechanism. The model firstly extracts the patients’ chief complaint texts features by convolution layer, and then introduces the attention mechanism to assign different weights to different features. Subsequently, these features are fused with the features extracted by RoBERTa for classification. In addition, this paper proposes a doctor recommendation algorithm that considers both patient similarity and patient preference. Through the in-depth analysis on the patients’ condition claims, various weights are assigned to various influencing factors, and then the matching degree is calculated to achieve the accurate recommendation of doctors. The experimental results reveal that the proposed department classification model’s accuracy on the dataset is 93.4%, and the Normalized Discounted Cumulative Gain (NDCG) of the doctor recommended algorithm is 90.7%. In this way, the proposed model effectively improves patient-doctoral matching with excellent performance.

Session context data integration to address the cold start problem in e-commerce recommender systems

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

Improving the accuracy of the information retrieval evaluation process by considering unjudged document lists from the relevant judgment sets

Introduction. To improve user satisfaction and loyalty to the search engines, the performance of the retrieval systems has to be better in terms of the number of relevant documents retrieved. This can be evaluated through the information retrieval evaluation process. This study identifies two methodologies that help to recover and better rank relevant information resources based on a query, while at the same time suppressing the irrelevant one. Method. A combination of techniques was used. Documents that were relevant and not retrieved by the systems were found from the document corpus and assigned new scores based on the Manifold fusion techniques then moved into the relevant judgment sets. Documents based on judgment sets and good contributing systems have been considered in the proposed methodologies. Analysis. Kendall Tau Correlation Coefficient, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Rank Biased Precision (Rbp) have been used to evaluate the performance of the methodologies. Results. The proposed methodologies outperformed the baseline works and enhanced the quality of the judgment sets, achieving a better result even with lesser pool depth. Conclusion. This research proposes two methodologies that increase the quality of the relevant documents in the judgment sets based on document similarity techniques and, thus, raise the evaluation process accuracy and reliability of the systems.

Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach

This study proposes how to incorporate concurrent purchase data into e-commerce recommendation systems to improve their predictive accuracy. We identified that concurrent purchases account for about 23% of total orders on Katcher’s, a Korean e-commerce platform. Despite the prevalence of concurrent purchases, existing algorithms often overlook this aspect. We introduce a novel transformer-based recommendation algorithm to process a user’s order history, including concurrent purchases. Each order is represented as a natural language sentence, capturing the order timestamp, product names and their attribute values, their corresponding categories, and whether multiple products were purchased together in a single order (i.e., a concurrent purchase). These sentences form a sequence, which serves as a training dataset for fine-tuning Bidirectional Encoder Representations from Transformers (BERT) with the Next Sentence Prediction objective. We validate our ideas by conducting experiments on Katcher’s platform, demonstrating the proposed method’s improved prediction performance compared to existing recommendation systems, with enhanced accuracy and F1 score. Notably, the normalized discounted cumulative gain (NDCG) showed a significant improvement with a large margin. Furthermore, we demonstrate the beneficial impact of integrating concurrent purchase information on prediction performance.

Quantum Nearest Neighbor Collaborative Filtering Algorithm for Recommendation System

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets.

Modeling multi-factor user preferences based on Transformer for next point of interest recommendation

With the rapid development of the mobile network and the gradual popularization of mobile devices, more and more users try to find attractive places to visit through WeChat, Twitter applications. In this trend, personalized next point of interest(POI) recommendation in the Location-based Social Network (LBSN) has become the focus of research and practice. Most existing studies capture user interest changes between different days (i.e. weekend and weekday), however, they ignore seasonal factors in time transition and category factors and thus fail to capture seasonal-level and category-level movement patterns in users’ mobile trajectories. Besides, they neglect the relevance between POIs from all users’ trajectory and fail to generate expressive POI embedding representation without constructing trajectory graph, which will reduce the accuracy of the next POI recommendation. To address the issues above, a next POI recommendation method for modeling Multi-factor User Preferences based on Transformer (MUPT) is developed, which consists of a global POI relationship modeling, a local multi-factor user preference modeling and a prediction module. It first learns the collaborative information of users with similar behavior to generate expressive POI embedding representation. Then it captures the personalized movement patterns of users at the POI, category and time levels based on Transformer mechanism in the local module. Especially the seasonal and other fine-grained information on the time series are learned in the time preference modeling part. The prediction module designed tracks the relationship between multi-level motion pattern representation of user check-in behavior and the next POI accessed by the user, and it finally obtains user’s preference probability for next POIs. An extensive experiment has been conducted on four datasets, and the experimental results analysis demonstrates that our proposed MUPT method is superior to other methods in terms of accuracy(ACC), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG).

Adaptive self-supervised learning for sequential recommendation

Sequential recommendation typically utilizes deep neural networks to mine rich information in interaction sequences. However, existing methods often face the issue of insufficient interaction data. To alleviate the sparsity issue, self-supervised learning is introduced into sequential recommendation. Despite its effectiveness, we argue that current self-supervised learning-based (i.e., SSL-based) sequential recommendation models have the following limitations: (1) using only a single self-supervised learning method, either contrastive self-supervised learning or generative self-supervised learning. (2) employing a simple data augmentation strategy in either the graph structure domain or the node feature domain. We believe that they have not fully utilized the capabilities of both self-supervised methods and have not sufficiently explored the advantages of combining graph augmentation schemes. As a result, they often fail to learn better item representations. In light of this, we propose a novel multi-task sequential recommendation framework named Adaptive Self-supervised Learning for sequential Recommendation (ASLRec). Specifically, our framework combines contrastive and generative self-supervised learning methods adaptively, simultaneously applying different perturbations at both the graph topology and node feature levels. This approach constructs diverse augmented graph views and employs multiple loss functions (including contrastive loss, generative loss, mask loss, and prediction loss) for joint training. By encompassing the capabilities of various methods, our model learns item representations across different augmented graph views to achieve better performance and effectively mitigate interaction noise and sparsity. In addition, we add a small proportion of random uniform noise to item representations, making the item representations more uniform and mitigating the inherent popularity bias in interaction records. We conduct extensive experiments on three publicly available benchmark datasets to evaluate our model. The results demonstrate that our approach achieves state-of-the-art performance compared to 14 other competitive methods: the hit rate (HR) improved by over 14.39%, and the normalized discounted cumulative gain (NDCG) increased by over 18.67%.

Similarity-based Graph Convolution Collaborative Recommendation Approach

With the rapid and iterative development of science and technology, a large amount of information exceeds the range that can be accepted, processed, or effectively utilized by an individual or a system, and recommendation algorithms can, to a certain extent, solve such problems, but traditional recommendation algorithms do not have a good solution to the problems related to data sparsity and recommendation accuracy. A similarity-based graph convolutional neural collaborative recommendation method (GCSCF) is proposed. The similarity algorithm based on the attributes of the item is used to find the item with the highest similarity and has not interacted with the current user, and this item is set to interact with the current user. The relevant interaction information of the user and the item is converted into relative feature vectors; the feature vectors are propagated using a graph convolutional neural network to aggregate the localized information, and the weight coefficients based on the item ratings are normalized to reduce the noise caused by the information aggregation. Comparative experiments are conducted on two public datasets, MovieLens-1M and Movielens-100K, with five baseline models on the set, and using Recall, Normalized Discounted Cumulative Gain (NDCG), and Precision as the evaluation metrics, and the results of the experiments show that the performance of the proposed social recommendation model better than other models.

Comparing discriminating abilities of evaluation metrics in link prediction

Link prediction aims to predict the potential existence of links between two unconnected nodes within a network based on the known topological characteristics. Evaluation metrics are used to assess the effectiveness of algorithms in link prediction. The discriminating ability of these evaluation metrics is vitally important for accurately evaluating link prediction algorithms. In this study, we propose an artificial network model, based on which one can adjust a single parameter to monotonically and continuously turn the prediction accuracy of the specifically designed link prediction algorithm. Building upon this foundation, we show a framework to depict the effectiveness of evaluating metrics by focusing on their discriminating ability. Specifically, a quantitative comparison in the abilities of correctly discerning varying prediction accuracies was conducted encompassing nine evaluation metrics: Precision, Recall, F1-Measure, Matthews correlation coefficient, balanced precision, the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AUPR), normalized discounted cumulative gain (NDCG), and the area under the magnified receiver operating characteristic. The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics.

An intelligent recommendation strategy for integrated online courses in vocational education based on short-term preferences

With the swift advancement of online teaching in vocational education, an increasing number of web-based course materials are being made available to students, granting them the freedom to select resources that suit their personal needs. To optimize the effectiveness of artificial intelligence-enabled smart vocational education, this study presents a course recommendation model centered on learning behaviors and interests. The model utilizes short-term preferences reconstruction behavior contribution to identify fluctuations in learners' interests in real-time. A model for recommending courses is proposed based on short-term preferences and enhancements to learning behavior. Its purpose is to tackle the issue of generalization arising from sparsity and weak correlation in learning behavior. The outcomes demonstrated the model put forth in the study achieved higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) values in comparison experiments with multiple models. Hence, this suggested that creating a novel component of historical learning behavior, powered by dynamic interest factors, could resolve the issue of changing learning interests and enhance the efficacy of course recommendation models. Furthermore, the introduction of a correlation mapping network enables the forward mapping transformation from weak to strong learning behavior, thus improving and optimizing input for the agent strategy, reducing data sparsity, and enhancing the performance and generalization of the course recommendation model.

A recommender system-using novel deep network collaborative filtering

<p>The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.</p>

Integrating listwise ranking into pairwise-based image-text retrieval

Image-Text Retrieval (ITR) is essentially a ranking problem. Given a query caption, the goal is to rank candidate images by relevance, from large to small. The current ITR datasets are constructed in a pairwise manner. Image-text pairs are annotated as positive or negative. Correspondingly, ITR models mainly use pairwise losses, such as triplet loss, to learn to rank. Pairwise-based ITR increases positive pair similarity while decreasing negative pair similarity indiscriminately. However, the relevance between dissimilar negative pairs is different. Pairwise annotations cannot reflect this difference in relevance. In the current datasets, pairwise annotations miss many correlations. There are many potential positive pairs among the pairs labeled as negative. Pairwise-based ITR can only rank positive samples before negative samples, but cannot rank negative samples by relevance. In this paper, we integrate listwise ranking into conventional pairwise-based ITR. Listwise ranking optimizes the entire ranking list based on relevance scores. Specifically, we first propose a Relevance Score Calculation (RSC) module to calculate the relevance score of the entire ranked list. Then we choose the ranking metric, Normalised Discounted Cumulative Gain (NDCG), as the optimization objective. We apply a metric smoothing method named Smooth-NDCG (S-NDCG) to ITR, which transforms the non-differentiable NDCG into a differentiable listwise loss. Our listwise ranking approach can be plug-and-play integrated into current pairwise-based ITR models. Experiments on ITR benchmarks show that integrating listwise ranking can improve the performance of current ITR models and provide more user-friendly retrieval results. The code is available at https://github.com/AAA-Zheng/Listwise_ITR.

Bio Mathematical Analysis on the Evaluation Scheme for Large Scale Innovation Competition

Large-scale innovation contests are becoming increasingly popular globally, with many innovators and enterprises actively participating. Work allocation optimization and fair judging are two significant concerns in innovation contests. This academic paper aims to recommend an optimized” cross-assignment” program by leveraging the optimal objective function method to improve the comparability of scores given by various judges. We conducted data-based descriptive statistical analysis and concluded that the two-stage and weighted evaluation schemes are more beneficial than the traditional judging scheme. Nonetheless, there are still some shortcomings that require addressing. To enhance fairness, we propose an improved two-stage evaluation scheme. In the first stage, we normalize the scores with a normal distribution. In the second stage of the process, we implement a system utilizing the Borda sorting technique to categorize submissions into five distinct groups for judges to evaluate based on their perceptions. We also detail a method for weighting tied scores to determine the final rankings. Testing indicates that this approach yields a Normalized Discounted Cumulative Gain (NDCG) of 0.8667, implying greater fairness and precision in the assessment of submissions.

Evaluating Semantic and Personalized Information Retrieval with Deep Learning Models: A Performance Metrics Analysis

Evaluation of the Information Retrieval (IR) model is an important, complex process that involves evaluating many aspects of performance using quantitative measurements called evaluation metrics. This work explores the efficacy of Deep learning (DL) models in personalized and semantic information retrieval (SIR), an area that has witnessed significant advancements in the last few years. A thorough analysis of several DL models for search engine index retrieval is provided. The particular task, relevant standards, and desirable qualities, among others, influence the use of evaluation metrics in IR. A comprehensive evaluation of the performance of an IR system uses several metrics. The paper emphasizes how DL models can produce IR results that are more accurate, with an emphasis on choosing important metrics for evaluation. Among these metrics, on a given input query, a recall of 1.0 indicates full coverage in detecting pertinent cases, while a precision of 0.5 indicates 50% accuracy in positive predictions on a given input query. At 0.6667, the F1-score strikes a compromise between recall and precision. The model's ability to rank and prioritize is assessed by Mean Average Precision (MAP) at 0.5426 and Normalized Discounted Cumulative Gain (NDCG) at 0.8896. Precision in the top 5 suggestions is evaluated by Precision@5, which has a value of 0.6. This is important in situations where user attention or recommendation space are restricted. These measurements offer priceless information on the efficacy of IR models.