Item Features Research Articles

Asymmetric multilevel interactive attention network integrating reviews for item recommendation

Recently, most studies in the field have focused on integrating reviews behind ratings to improve recommendation performance. However, two main problems remain (1) Most works use a unified data form and the same processing method to address the user and the item reviews, regardless of their essential differences. (2) Most works only adopt simple concatenation operation when constructing user-item interaction, thus ignoring the multilevel relationship between the user and the item, which may lead to suboptimal recommendation performance. In this paper, we propose a novel Asymmetric Multi-Level Interactive Attention Network (AMLIAN) integrating reviews for item recommendation. AMLIAN can predict precise ratings to help the user make better and faster decisions. Specifically, to address the essential difference between the user and the item reviews, AMLIAN uses the asymmetric network to construct user and item features using different data forms (document-level and review-level). To learn more personalized user-item interaction, the user ID and item ID and some processed features of user reviews and item reviews are respectively used for multilevel relationships. Experiments on five real-world datasets show that AMLIAN significantly outperforms state-of-the-art methods.

Netflix Movies Recommendation System

Netflix is a leading global entertainment company with over 247 million paying customers who can access TV series, films, and games in over 190+ countries. Many languages and genres are offered for the content. As much as they like, whenever and wherever they wish, participants are free to change their plans and play, pause, and resume viewing. With the exponential growth of content on streaming platforms like Netflix, users often face the challenge of finding relevant and enjoyable movies.The "Cold-Start" issue is a recommendation system issue wherein new users or items have little data, making it challenging to provide appropriate recommendations. In response, this paper proposes a data-driven movie recommendation system tailored for Netflix. Leveraging machine learning techniques, user preferences, and historical data, the model aims to enhance the user experience by providing personalized movierecommendations. This study explores algorithms such as collaborative filtering, content-based filtering, and hybrid models to achieve accurate and effective movie recommendations. While content-based filtering uses item features to suggest shows or movies a user has previously enjoyed, collaborative filtering examines user behavior and preferences to produce suggestions. Even with insufficient data, the system can deliver more accurate recommendations for new users or things by combining various approaches. By making recommendations based on both item features and user behavior, this hybrid approach helps to reduce the cold start issue.

Multimodal Contrastive Transformer for Explainable Recommendation

Explanations play an essential role in helping users evaluate results from recommender systems. Various natural language generation methods have been proposed to generate explanations for the recommendation. However, they usually suffer from two problems. First, since user-provided review text contains noisy data, the generated explanations may be irrelevant to the recommended items. Second, as lacking some supervision signals, most of the generated sentences are similar, which cannot meet the diversity and personalized needs of users. To tackle these problems, we propose a multimodal contrastive transformer (MMCT) model for an explainable recommendation, which incorporates multimodal information into the learning process, including sentiment features, item features, item images, and refined user reviews. Meanwhile, we propose a dynamic fusion mechanism during the decoding stage, which generates supervision signals to guide the explanation generation. Additionally, we develop a contrastive objective to generate diverse explainable texts. Comprehensive experiments on two real-world datasets show that the proposed model outperforms comparable explainable recommendation baselines in terms of explanation performance and recommendation performance. Efficiency analysis and robustness analysis verify the advantages of the proposed model. While ablation analysis establishes the relative contributions of the respective components and various modalities, the case study shows the working of our model from an intuitive sense.

Preference Aware Dual Contrastive Learning for Item Cold-Start Recommendation

Existing cold-start recommendation methods often adopt item-level alignment strategies to align the content feature and the collaborative feature of warm items for model training, however, cold items in the test stage have no historical interactions with users to obtain the collaborative feature. These existing models ignore the aforementioned condition of cold items in the training stage, resulting in the performance limitation. In this paper, we propose a preference aware dual contrastive learning based recommendation model (PAD-CLRec), where the user preference is explored to take into account the condition of cold items for feature alignment. Here, the user preference is obtained by aggregating a group of collaborative feature of the warm items in the user's purchase records. Then, a group-level alignment between the user preference and the item's content feature can be realized via a proposed preference aware contrastive function for enhancing cold-item recommendation. In addition, a joint objective function is introduced to achieve a better trade-off between the recommendation performance of warm items and cold items from both item-level and group-level perspectives, yielding better overall recommendation performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method, and the results show the superiority of our method, as compared with the state-of-the-arts.

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introduce item features (e.g., thumbnails) for cold-start item recommendation. They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction. Unfortunately, the features of cold-start items, especially the popular ones, tend to diverge from those of warm-start ones due to temporal feature shifts, preventing the feature extractor from accurately learning feature representations of cold-start items. To alleviate the impact of temporal feature shifts, we consider using Distributionally Robust Optimization (DRO) to enhance the generation ability of the feature extractor. Nonetheless, existing DRO methods face an inconsistency issue: the worse-case warm-start items emphasized during DRO training might not align well with the cold-start item distribution. To capture the temporal feature shifts and combat this inconsistency issue, we propose a novel temporal DRO with new optimization objectives, namely, 1) to integrate a worst-case factor to improve the worst-case performance, and 2) to devise a shifting factor to capture the shifting trend of item features and enhance the optimization of the potentially popular groups in cold-start items. Substantial experiments on three real-world datasets validate the superiority of our temporal DRO in enhancing the generalization ability of cold-start recommender models.

Explanatory Cognitive Diagnosis Models Incorporating Item Features.

Item quality is crucial to psychometric analyses for cognitive diagnosis. In cognitive diagnosis models (CDMs), item quality is often quantified in terms of item parameters (e.g., guessing and slipping parameters). Calibrating the item parameters with only item response data, as a common practice, could result in challenges in identifying the cause of low-quality items (e.g., the correct answer is easy to be guessed) or devising an effective plan to improve the item quality. To resolve these challenges, we propose the item explanatory CDMs where the CDM item parameters are explained with item features such that item features can serve as an additional source of information for item parameters. The utility of the proposed models is demonstrated with the Trends in International Mathematics and Science Study (TIMSS)-released items and response data: around 20 item linguistic features were extracted from the item stem with natural language processing techniques, and the item feature engineering process is elaborated in the paper. The proposed models are used to examine the relationships between the guessing/slipping item parameters of the higher-order DINA model and eight of the item features. The findings from a follow-up simulation study are presented, which corroborate the validity of the inferences drawn from the empirical data analysis. Finally, future research directions are discussed.

The Effects of Canonity and the Authorial Weight in Translating Men in Sun’ Ghassan Kanafani into English Language

This study deals with canonity in the translation from Arabic to English of Authorial Weight and Translator Authority in the novella "Men in Sun". It debates that if Men in Sun "Rejal fel AL-Shamas" that written by famous author in Arab world Ghassn Kanafani Palestinian writer that defend on the Palestinian issue, the above mentioned novella translated to English by Dr. Hilary Kilpatrick gave the same value to the TT as it appears in the ST during translating accurately the features of figurative language idioms, religious expressions, metaphors, symbolism, and cultural items with choosing randomly examples from the novel. According to strategies found in the translation based on Vinay and Darbelnet’s (1995) including direct and Oblique translation and Newmark’s (1988). moreover, the study stresses the value of perception the goal, emotiveness, ideology embedded in Arabic novel Men in Sun "Rejal fel AL-Shamas" when the translator seeks to equal between the TT and ST. The aesthetic features that notable in Arabic that really the translator attempts to reddened it in English to give the sense of the sentences. In short, involves that the translation of literally work is affected by the canonity and aesthetic features that applied.

Diarec: Dynamic Intention-Aware Recommendation with Attention-Based Context-Aware Item Attributes Modeling

Abstract Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has increased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other state-of-the-art methods.

A multi-variable service supply management portfolio model for Telecom industry

Supply operations require some kind of classification according to the features of items. Based on the literature review and expert interview, a multi-variable service supply management portfolio model was developed to classify the procurable service items in the Telecom industry in a more realistic way with better precision. First, a multi-level relational reflective model was built based on four dimensions using the fuzzy Delphi method and the model was validated by factor analysis. Then, 50 service items were placed in 16 clusters of the portfolio model. For each category of services in the model, the pertinent supplier relationship management practices were proposed using the literature review accompanied by a focus group. The presented model contributed to the development of previous theoretical trends in supply management portfolio models by introducing a new dimension, called “pace of innovation in technology”, to those extracted from the literature review, such as “strategic importance of the items”, “supply risk”, and “extent of buyer captivity”. Despite the classical models targeting tangible goods and products, our model was customized for the service industry context and can be considered a starting point towards service supply market segmentation procedures according to the attributes of each service item category.

Multimodal representation learning for tourism recommendation with two-tower architecture.

Personalized recommendation plays an important role in many online service fields. In the field of tourism recommendation, tourist attractions contain rich context and content information. These implicit features include not only text, but also images and videos. In order to make better use of these features, researchers usually introduce richer feature information or more efficient feature representation methods, but the unrestricted introduction of a large amount of feature information will undoubtedly reduce the performance of the recommendation system. We propose a novel heterogeneous multimodal representation learning method for tourism recommendation. The proposed model is based on two-tower architecture, in which the item tower handles multimodal latent features: Bidirectional Long Short-Term Memory (Bi-LSTM) is used to extract the text features of items, and an External Attention Transformer (EANet) is used to extract image features of items, and connect these feature vectors with item IDs to enrich the feature representation of items. In order to increase the expressiveness of the model, we introduce a deep fully connected stack layer to fuse multimodal feature vectors and capture the hidden relationship between them. The model is tested on the three different datasets, our model is better than the baseline models in NDCG and precision.

Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.

Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder

Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This opaque decision-making method can greatly affect users’ trust in the recommendation system. Therefore, finding a way to explain the reasons for model decisions has become an urgent task. Previous studies often used LSTM and other models to generate recommendation explanations and explain the reasons for recommendations in text form. However, traditional methods cannot effectively use the ID information of users and items, and the text generated is highly repetitive. To solve this problem, this paper uses the method of prompt learning combined with a graph encoder to design a recommendation explanation generation model. In order to narrow the semantic gap between the ID information of users and items and natural language and capture high-level interaction information, this paper designs a graph encoder based on user similarity to learn the interactive semantic information of user and item IDs, and to construct a continuous prompt. Then, the discrete prompt composed of discrete features of users and items is combined with the continuous prompt to construct a hybrid prompt to input into the pre-trained model to generate the recommended explanation. This paper experiments on three publicly available datasets and compares them with several state-of-the-art methods to demonstrate the personalization and text quality of the generated explanations.

Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based Recommendation

Session-based recommendation is an important part of many e-commerce websites. Its purpose is to make recommendations based on the interaction behavior of anonymous users in a short period of time. Graph neural network can capture complex interactions in sessions, and they are a class of methods with better performance among existing session-based recommendation models. However, most existing models only learn item features for a single session. At the same time, GNNs are not good at capturing long-distance dependencies in a session, which leads to limited performance improvements for them. To address this deficiency, we propose sequence-aware graph neural network incorporating neighborhood information, named SAN-GNN. We construct a session graph and a neighborhood graph to learn item representations. For neighborhood graph, we propose a neighborhood Information extractor for the neighborhood graph to learn the neighbor information of nodes on the neighborhood graph. For the graph model of the session layer, we propose a session graph attention(SGA) module to learn the item representation of the target session. SGA uses Ta-LSTM to learn sequential dependencies in the target session and uses GCN with an integrated attention mechanism to learn node feature relevant to the target item. Exhaustive experiments on three public real-world datasets show that SAN-GNN outperforms the most advanced existing session-based method.

BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.

Aggregating knowledge and collaborative information for sequential recommendation

Sequential recommendation aims to predict users’ future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users’ current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.

FDGNN: Feature-Aware Disentangled Graph Neural Network for Recommendation

Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.

SMLP4Rec: An Efficient All-MLP Architecture for Sequential Recommendations

Self-attention models have achieved the state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user–item interactions. However, they rely on adding positional embeddings to the item sequence to retain the sequential information, which may break the semantics of item embeddings due to the heterogeneity between these two types of embeddings. In addition, most existing works assume that such dependencies exist solely in the item embeddings, but neglect their existence among the item features. In our previous study, we proposed a novel sequential recommendation model, i.e., MLP4Rec, based on the recent advances of MLP-Mixer architectures, which is naturally sensitive to the order of items in a sequence because matrix elements related to different positions of a sequence will be given different weights in training. We developed a tri-directional fusion scheme to coherently capture sequential, cross-channel, and cross-feature correlations with linear computational complexity as well as much fewer model parameters than existing self-attention methods. However, the cascading mixer structure, the large number of normalization layers between different mixer layers, and the noise generated by these operations limit the efficiency of information extraction and the effectiveness of MLP4Rec. In this extended version, we propose a novel framework – SMLP4Rec for sequential recommendation to address the aforementioned issues. The new framework changes the flawed cascading structure to a parallel mode, and integrates normalization layers to minimize their impact on the model’s efficiency while maximizing their effectiveness. As a result, the training speed and prediction accuracy of SMLP4Rec are vastly improved in comparison to MLP4Rec. Extensive experimental results demonstrate that the proposed method is significantly superior to the state-of-the-art approaches. The implementation code is available online to ease reproducibility.

Joint inter-word and inter-sentence multi-relation modeling for summary-based recommender system

Review is an essential piece of information that influences users’ decisions, but excessively long reviews not only degrade the user experience but also affect the accuracy of the recommender system. Therefore, Joint Inter-Word and Inter-Sentence Multi-Relation Modeling for the Summary-based Recommender System (MRSR) is proposed in this paper. In MRSR, the concise summary information serves as representation data, and a multi-relation modeling module is designed to construct user and item characteristics from two levels. Specifically, the inter-word relation modeling module, which consists of the Transformer and the pooling layer, is used to learn the long dependencies of summaries and extract word-level features by calculating the relative weights between words within sentences. The inter-sentence relation modeling module is used to enrich the sentence-level features of users and items, where an attention mechanism is employed to perceive the relative weights between different summary sentences. Finally, the fusion layer based on multi-head attention and the prediction layer based on attentional factorization machine are implemented to conduct the shallow and deep interactions between user and item features, based on which MRSR completes the final rating prediction. Extensive experimental results on five publicly available datasets demonstrate that MRSR achieves a 5.94% improvement in RMSE metrics compared to state-of-the-art methods. Furthermore, the accuracy of most existing models is improved by 1%∼2% while the inference time is reduced by 10% by utilizing summaries as representation data. It proves the efficiency and effectiveness of our proposed approach, which has promising application prospects.

Prediction of postoperative infectious complications in elderly patients with colorectal cancer: a study based on improved machine learning

BackgroundInfectious complications after colorectal cancer (CRC) surgery increase perioperative mortality and are significantly associated with poor prognosis. We aimed to develop a model for predicting infectious complications after colorectal cancer surgery in elderly patients based on improved machine learning (ML) using inflammatory and nutritional indicators.MethodsThe data of 512 elderly patients with colorectal cancer in the Third Affiliated Hospital of Anhui Medical University from March 2018 to April 2022 were retrospectively collected and randomly divided into a training set and validation set. The optimal cutoff values of NLR (3.80), PLR (238.50), PNI (48.48), LCR (0.52), and LMR (2.46) were determined by receiver operating characteristic (ROC) curve; Six conventional machine learning models were constructed using patient data in the training set: Linear Regression, Random Forest, Support Vector Machine (SVM), BP Neural Network (BP), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost) and an improved moderately greedy XGBoost (MGA-XGBoost) model. The performance of the seven models was evaluated by area under the receiver operator characteristic curve, accuracy (ACC), precision, recall, and F1-score of the validation set.ResultsFive hundred twelve cases were included in this study; 125 cases (24%) had postoperative infectious complications. Postoperative infectious complications were notably associated with 10 items features: American Society of Anesthesiologists scores (ASA), operation time, diabetes, presence of stomy, tumor location, NLR, PLR, PNI, LCR, and LMR. MGA-XGBoost reached the highest AUC (0.862) on the validation set, which was the best model for predicting postoperative infectious complications in elderly patients with colorectal cancer. Among the importance of the internal characteristics of the model, LCR accounted for the highest proportion. Conclusions: This study demonstrates for the first time that the MGA-XGBoost model with 10 risk factors might predict postoperative infectious complications in elderly CRC patients.

TryonCM2: Try-on-Enhanced Fashion Compatibility Modeling Framework.

Recently, fashion compatibility modeling, which can score the matching degree of several complementary fashion items, has gained increasing research attention. Previous studies have primarily learned the features of fashion items and utilize their interaction as the fashion compatibility. However, the try-on looking of an outfit help us to learn the fashion compatibility in a combined manner, where items are spatially distributed and partially covered by other items. Inspired by this, we design a try-on-enhanced fashion compatibility modeling framework, named TryonCM2, which incorporates the try-on appearance with the item interaction to enhance the fashion compatibility modeling. Specifically, we treat each outfit as a sequence of items and adopt the bidirectional long short-term memory (LSTM) network to capture the latent interaction of fashion items. Meanwhile, we synthesize a try-on template image to depict the try-on appearance of an outfit. And then, we regard the outfit as a sequence of multiple image stripes, i.e., local content, of the try-on template, and adopt the bidirectional LSTM network to capture the contextual structure in the try-on appearance. Ultimately, we combine the fashion compatibility lying in the item interaction and try-on appearance as the final compatibility of the outfit. Both the objective and subjective experiments on the existing FOTOS dataset demonstrate the superiority of our framework over the state-of-the-art methods.