Collaborative Filtering Methods Research Articles

A Novel Algorithmic Similarity Measure for Collaborative Filtering: A Recommendation System Based on Rating Distances

Internet sources contain a vast amount of information about items that people desire to purchase. It is impossible to evaluate these resources and come to an informed decision. People need automated systems that evaluate previous information and propose item alternatives. Recommending items using a smart system, which is based on the previous user preferences, has growing importance since the available product data is exponentially growing. Additionally, it is difficult to find new and correct things that a user would like among this massive amount of data. To make accurate recommendations with a smart system, researchers and practitioners use collaborative filtering methods with similarity calculation based on user preferences. The crucial point in collaborative filtering is to find a valuable measure that resembles correct similarity between users. The current similarity metrics in the literature have some disadvantages in conducting accurate recommendations. To improve the recommendation performance, this study proposes a novel similarity measure that assesses the distance between the user’s ratings and the median score. Considering distance from the median score is essential since some users may prefer to rate close to the median rather than the extremes. Experiments were conducted with a famous collaborative filtering dataset. Results showed that proposed similarity measure demonstrated superior performance regarding the recommendation accuracy. Implications of our results for XYZ are discussed.

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user’s historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user’s trustee have for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.

Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative filtering method, which integrates content features and temporal information. First, we construct an Attention-LSTM model to capture the popularity trends and exploit the temporal information based on users’ feedback; finally, we use the concatenate method to fuse temporal information and content features and design a Deep Attention Popularity Prediction (DAVPP) algorithm to solve DAFCT. We experimentally adjust the weighted composite similarity metric parameters of Query pages and verify the scalability of the algorithm. Experimental results on the KDDCUP2012 dataset show that this model collaborative filtering and recommendation algorithm has better scalability and better recommendation quality. Compared with the Attention-LSTM model and the NFM model, the F1 score of DAFCT is improved by 9.80 and 3.07 percentage points, respectively.

Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

Sistem Rekomendasi Desain Jersey Menggunakan Metode Item Based Collaborative Filtering


 Creative Color Gresik is a convection company engaged in the production of jersey making which is located in the city of Gresik, East Java. The production carried out is jersey printing for sports clothes to clothes for the team. The problem that is obtained after observing is the customer who wants to order a jersey at PT. Creative Col-or Gresik still doesn't have a picture of the jersey he wants to make. The number of designs that exist in PT. Creative Color Gresik, makes customers feel confused in making choices. Each design has a different motif and color. The development of a jersey design recommendation system using the item based collaborative filtering method can assist in providing jersey design recommendations that can assist customers in determining the design to be made according to the results of the recommendations generated. The test results of the item based collaborative filtering method using questionnaire testing obtained an accuracy of 75% and an error of 25%.

Neural matrix factorization++ based recommendation system.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs' latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.

Improving the web-based recommender system through collaborative filtering

The solution to the problem of improving the recommendation web-oriented system through the use of collaborative filtering to provide recommendations to users of the web service with articles on gaming topics is presented. In the course of the research, a set of stages of development and improvement of the recommendatory web-oriented system was systematized. The structure of the database and the method of collaborative filtering using the proposed similarity measure to form recommendations for users are presented. The preliminary structuring of articles by categories was used to identify user preferences and provide recommendations. The use of collaborative filtering with the proposed similarity measure allowed us to increase the number of articles viewed by users. Ill.: 2. Bibliography: 10 titles.

A METHOD OF COMPUTER SIMULATION MODELING OF USER AND BOT BEHAVIOR IN A RECOMMENDATION SYSTEM USING THE GRAPH DATABASE NEO4J

The subject matter of the article is the process of computer simulation modeling of complex networks. The goal is to develop a method of computer simulation modeling of ordinary user and bot behavior in a recommendation system based on the theory of complex networks to test the accuracy and robustness of various algorithms for generating recommendations. The tasks to be solved are: to develop a computer simulation model of user and bot behavior in a recommendation system with the ability to generate datasets for testing recommendation generation algorithms. The methods used are: graph theory, theory of complex networks, statistics theory, probability theory, methods of object-oriented programming and methods of working with graph databases. Results. A method of computer simulation modeling of users and objects in a recommender system was proposed, which consists of generating the structure of the social graph of a recommender system and simulating user and bot behavior in it. A series of experiments to test the performance of the developed computer simulation model was carried out. During the experiments, working and testing datasets were generated. Based on the working datasets, the preferences of users by the method of collaborative filtering were predicted. Based on testing datasets, the accuracy of prediction predictions was checked. The results of the experiments showed that the jitter of the investigated values of the Precision, Recall and RMSE of prediction predictions in most practical cases confidently fits within the allowable fluctuation limits, and therefore the users' behavior in computer simulation model was not random and it real users' behavior with certain preferences was simulated. This confirms the reliability of the developed computer simulation model of a recommendation system. Conclusions. A method of computer simulation modeling of user and bot behavior in a recommendation system, which allows generating datasets for testing the algorithms for generating recommendations, was proposed. The developed method makes it possible to simulate the behavior of both ordinary users and bots, which makes it possible to create datasets for testing the robustness of recommender systems to information attacks, as well as for testing the effectiveness of methods for detecting and neutralizing botnets. The structure of relations between users and objects of the recommender system was modeled using the theory of complex networks. Information attacks of bots were modeled on the basis of known models of profile-injection attacks on recommender systems.

A light heterogeneous graph collaborative filtering model using textual information

Due to the development of graph neural networks, graph-based representation learning methods have made great progress in recommender systems. However, data sparsity is still a challenging problem that most graph-based recommendation methods are confronted with. Recent works try to address this problem by utilizing side information. In this paper, we exploit the relevant and easily accessible textual information by advanced natural language processing (NLP) models and propose a light RGCN-based (RGCN, relational graph convolutional network) collaborative filtering method on heterogeneous graphs. Specifically, to incorporate rich textual knowledge, we utilize a pre-trained NLP model to initialize the embeddings of text nodes. Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity. Moreover, the matching function used by most graph-based representation learning methods is the inner product, which is not appropriate for the obtained embeddings that contain complex semantics. We design a predictive network that combines graph-based representation learning with neural matching function learning, and demonstrate that this architecture can bring a significant performance improvement. Extensive experiments are conducted on three publicly available datasets, and the results verify the superior performance of our method over several baselines.

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

PurposeThis paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.Design/methodology/approachFirst, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.FindingsFolksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services

Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media data can be exploited to improve their reliability. In the case of media social data, sentiment analysis of the opinions expressed by users, together with properties of the items they consume, can help gain a better understanding of their preferences. In this study, we present a recommendation approach that integrates sentiment analysis and genre-based similarity in collaborative filtering methods. The proposal involves the use of BERT for genre preprocessing and feature extraction, as well as hybrid deep learning models, for sentiment analysis of user reviews. The approach was evaluated on popular public movie datasets. The experimental results show that the proposed approach significantly improves the recommender system performance.

Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

The rapid development of the tourism industry and the Internet era has led to an increasingly severe problem of travel information overload, and travel recommendation methods are essential to solving the information overload problem. Traditional recommendation algorithms only target common travel scenarios during the daytime, combining the ratings and necessary attributes between users and items to calculate similarity for a recommendation. Still, the research on night travel recommendations is one of the few scenarios that needs to be explored urgently. This paper, based on histogram equalization, achieves better experimental results on image enhancement, combines convolutional neural network technology with night image and text comment feature extraction technology, and evaluates the resulting error with mean absolute error (MAE). This paper presents the first night travel recommendation system. It compares it with the traditional collaborative filtering method, and the model proposed in this paper can effectively reduce the prediction error.

A Sequential Decision Approach to Ordinal Preferences in Recommender Systems

We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, evaluates against the latent utility at the corresponding level and moves up until a suitable ordinal level is found. Crucial to this generative process is the underlying utility random variables that govern the generation of ratings and their modelling choices. To this end, we make a novel use of the generalised extreme value distributions, which is found to be particularly suitable for our modeling tasks and at the same time, facilitate our inference and learning procedure. The proposed approach is flexible to incorporate features from both the user and the item. We evaluate the proposed framework on three well-known datasets: MovieLens, Dating Agency and Netflix. In all cases, it is demonstrated that the proposed work is competitive against state-of-the-art collaborative filtering methods.

An Approach to Integrating Sentiment Analysis into Recommender Systems.

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

Content-Aware Listwise Collaborative Filtering

Recently, listwise collaborative filtering (CF) algorithms are attracting increasing interest due to their efficiency and prediction quality. Different from rating-oriented (pointwise) CF, they recommend a preference ranking of items to each user without estimating the absolute value of the ratings. In practice, there are extensive side information about users and items in many corpora that can improve recommendation performance and specially help CF methods to address the cold-start problem. However, a model has not been proposed that incorporates side information to listwise CF. Therefore, in this work, a Bayesian graphical model, called Content-Aware Listwise Collaborative Filtering (CALCF), is developed which incorporates text information, represented as a bag of words, to listwise CF using topic models. We propose a Gibbs sampler with closed-form samples using data augmentation techniques to infer the latent variables. CALCF has been validated by comparison with previous listwise and pointwise algorithms on implicit (click, view, purchase) and explicit (numeric ratings) feedback data in both cold-start and warm-start scenarios. The results demonstrate that in most cases CALCF achieves better Normalized Discounted Cumulative Gain (NDCG) and Recall at top M recommendation compared with the previous models.

A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

Recommender systems are challenging research problems being exploited to suggest new items or services, such as books, music and movies, and even people, to users based on information about the user profile or the recommended items. To date, collaborative filtering (CF) has become one of the most widely used approaches for recommendations. However, traditional CF methods usually cannot track temporal dynamic user preferences and topic changes to make appropriate suggestions. Moreover, the performance of CF is limited in the case of sparse data. In this paper, we propose a novel temporal recommender system based on multiple transitions in user preference drift, called MTUPD, which employs a multitransition factor and a forgetting time function to investigate the evolution of user preferences. In addition, we consider addressing the rating sparsity issue by using text reviews. Understanding the reviews can facilitate the system grasping whether or not a user is attracted by the appearance of an item and whether the facet of an item’s appearance contributes the most to its ratings. To achieve this, we apply a topic model that automatically classifies hidden topic factors in each time period and incorporate the transition method for both user preferences and relevant review topics. Experiments show that our proposed model outperforms the compared models on eight promising datasets for temporal recommender systems.

Attention-based dynamic user preference modeling and nonlinear feature interaction learning for collaborative filtering recommendation

The traditional collaborative filtering (CF) method based on static user preference modeling and linear matching function learning severely limits the recommendation performance. To solve the above problem, in this article, we adopt dynamic user preference modeling and nonlinear matching function learning in the CF recommendation. For dynamic user preference modeling, a two-layer neural attention network is used, which fully considers the predicted item, the recent historical interacted items and their interaction time to estimate the contribution weight of each interacted item in user preferences modeling. For nonlinear matching function learning, we add a single hidden layer neural network on top of the traditional matrix factorization (MF) model, which can significantly improve the feature interaction learning capabilities of the model with only a few additional parameters. Extensive experiments show that our method significantly outperforms the state-of-the-art CF methods and the key technologies we proposed in this research have a positive effect on improving the recommendation performance.

ADCF: Attentive representation learning and deep collaborative filtering model

In this paper, we propose a deep collaborative filtering recommendation model, which consists of an attention-based representation learning component and a multi-input matching function learning component. This model takes interaction matrix based on implicit feedback as data source to construct representations of long-term user preferences and item latent features. In the representation learning, a time-aware attention network is used, which uses the embedding vectors of the predicted item, recent historical interaction items, and the interaction time of recent historical interaction items to estimate the weights of different historical interaction items to short-term user preferences modeling. Then, the dynamic user preference representation can be obtained by combining short-term preferences with long-term preferences. In the matching function learning, a multi-input deep learning model is used. Its input includes not only the dynamic user preference representation and the item latent feature representation, but also the linear interaction between the two representations, so that the model has more powerful feature interactions learning ability. Experimental results on four datasets from different domains show that our method is largely superior to the state-of-the-art collaborative filtering methods, and the novel techniques we propose in this paper are effective in improving recommendation performance.