Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy

Depth Limited Search pada metode Trust Inference berbasis Collaborative Filtering

ABSTRAKSI: Meningkatnya jumlah informasi di internet telah menyebabkan sulitnya seseorang untuk mendapatkan informasi yang mungkin akan berguna untuk orang tersebut. Diperlukan suatu sistem yang dapat menyaring informasi yang ada dan menyampaikan informasi itu kepada orang yang tepat.Dalam penelitian tugas akhir ini digunakan algoritma depth limited search pada metode trust inference berbasis collaborative filtering untuk melakukan perekomendasian item-item(movie) yang mungkin akan berguna untuk user yang akan diberikan rekomendasi dengan menghitung nilai prediksi rating item-item tersebut. Metode trust inference digunakan untuk mengurangi masalah sparsity yang sering terjadi pada recommender system berbasis collaborative filtering.Dari hasil pengujian, metode trust inference menunjukan performa/kualitas prediksi yang lebih baik ketika data yang digunakan bersifat sparse.Kata Kunci : Recommender System, Collaborative Filtering, Trust Inference, Depth Limited Search, SparsityABSTRACT: Increasing the amount of information on internet has made more difficult for someone to get information that might be useful for that person. a system is required for filter an existing information and deliver it to the right people.This final task research use depth limited search algorithm on trust inference method based on collaborative filtering to perform the recommendation of items(movie) that might be useful to user who will be given the recommendation by calculate the prediction value of items rating. Trust inference method used to reduce the sparsity problem that often occurs in collaborative filtering-based recommender systemsFrom the test results, the trust inference methods showed a better performance/prediction quality when the data used are sparse.Keyword: Recommender System, Collaborative Filtering, Trust Inference, Depth Limited Search, Sparsit

A Distributed Method for Trust-Aware Recommendation in Social Networks

This paper contains the details of a distributed trust-aware recommendation system. Trust-base recommenders have received a lot of attention recently. The main aim of trust-based recommendation is to deal the problems in traditional Collaborative Filtering recommenders. These problems include cold start users, vulnerability to attacks, etc.. Our proposed method is a distributed approach and can be easily deployed on social networks or real life networks such as sensor networks or peer to peer networks

A framework for collaborative filtering recommender systems

As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users? trust in these. This paper provides: (a) measures to evaluate the novelty of the users? recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust

An improved model for trust-aware recommender systems based on multi-faceted trust

As customers enjoy the convenience of online shopping today, they face the problem of selecting from hundreds of thousands of products. Recommender systems, which make recommendations by matching products to customers based on the features of the products and the purchasing history of customers, are increasingly being incorporated into e-commerce websites. Collaborative filtering is a major approach to design algorithms for these systems. Much research has been directed toward enhancing the performance of recommender systems by considering various psychological and behavioural factors affecting the behaviour of users, e.g. trust and emotion. While e-commerce firms are keen to exploit information on social trust available on social networks to improve their services, conventional trust-aware collaborative filtering does not consider the multi-facets of social trust. In this research, we assume that a consumer tends to trust different people for recommendations on different types of product. For example, a user trusts a certain reviewer on popular items but may not place as much trust on the same reviewer on unpopular items. Furthermore, this thesis postulates that if we, as online shoppers, choose to establish trust on an individual while we ourselves are reviewing certain products, we value this individual’s opinions on these products and we most likely will value his/her opinions on similar products in future. Based on the above assumptions, this thesis proposes a new collaborative filtering algorithm for deriving multi-faceted trust based on trust establishment time. Experimental results based on historical data from Epinions show that the new algorithm can perform better in terms of accuracy when compared with conventional algorithms

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.Comment: 10 pages, Accepted as a full paper on the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS'20

Credibility-based social network recommendation: Follow the leader

In Web-based social networks (WBSN), social trust relationships between users indicate the similarity of their needs and opinions. Trust can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is an aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations based on leaders' credibility in the "Follow the Leader" model as Top-N recommenders by incorporating social network information into user-based collaborative filtering. To demonstrate the feasibility and effectiveness of "Follow the Leader" as a new approach to making recommendations, first we develop a new analytical tool, Social Network Analysis Studio (SNAS), that captures real data and used it to verify the proposed model using the Epinions dataset. The empirical results demonstrate that our approach is a significantly innovative approach to making effective collaborative filtering based recommendations especially for cold start users. © 2010 Al-Sharawneh & Williams

Estimating Trust Strength For Supporting Effective Recommendation Services

In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend products that match users’ preferences and requirements. In addition to the well-known collaborative filtering recommendation approach, the trust-based recommendation approach is the emerging one. The reason is that most of online communities allow users to express their trust on other users. Based on the analysis of trust relationships, the trust-based recommendation approach finds out and consults the opinions of more reliable users and therefore makes better recommendations. Existing trust-based recommendation techniques consider all trust relationships in a given trust network equally important and give them the same trust strength. However, in a real-world setting, trust relationships may be of various strengths. In response, in this study, we propose a mechanism for trust strength estimation on the basis of the machine learning approach and estimate the trust strength for each existing trust relationship in a given trust network. To overcome the sparsity of the trust network, we also develop a modified trust propagation method to expand the original trust network. Finally, we perform a series of experiments to demonstrate the performance of our trust-based recommendation approach based on the trust strength estimation mechanism. Our empirical evaluation results show that our proposed approach outperforms our benchmark techniques, i.e., the traditional collaborative filtering approach and the original trust-based one

Analisis dan Implementasi Trust-aware Recommender System Berbasis Collaborative Filtering<br><br>Analysis and Implementation of Trust-aware Recommender System Based on Collaborative Filtering

ABSTRAKSI: Sebagai salah satu solusi dalam mengatasi masalah information overload, Recommender system berusaha memberikan rekomendasi berupa item yang mungkin disukai oleh user berdasarkan preferensi user tersebut. Salah satu metode yang paling banyak digunakan dalam recommender system adalah collaborative filtering. Namun Pure Collaborative Filtering masih belum mampu mengatasi beberapa masalah yang sering terdapat dalam suatu recommender system, misalnya masalah cold start user, data sparsity, dan serangan dari user yang jahat.Tugas akhir ini menganalisa penggunaan trust pada recommender system dan mengimplementasikannya pada sebuah Trust-aware recommender system. Penggunaan trust diharapkan dapat mengatasi permasalahan cold start user, data sparsity,dan serangan dari user yang sebelumnya belum dapat diatasi dengan baik menggunakan Pure Collaborative Filtering. Tugas akhir ini menganalisa akurasi serta jumlah rating yang dapat diprediksi pada Trust-aware recommender system dan membandingkannya dengan Pure Collaborative Filtering.Penggunaan trust pada recommender system dapat meningkatkan akurasi dan jumlah prediksi. Nilai MAE dan coverage yang dihasilkan pada Trust-aware recommender system lebih baik dibanding Pure Collaborative Filtering. Nilai yang sebaiknya dipilih sebagai jarak propagasi maksimal adalah 3 karena propagasi pada jarak ini telah mampu memberikan MAE dan coverage yang cukup baik pada hampir semua skenario pengujian.Kata Kunci : recommender system, collaborative filtering, trust, trust-aware, cold start user, sparsityABSTRACT: As one of solutions to overcome information overload, recommender system trying to provide item recommendation that may be useful for users based on their preferences. Most used method in recommender system is collaborative filtering. However, it is still not be able to solved some problems in recommender system such as cold start user, data sparsity, and attack by malicious user.This final project analyze the use of trust in recommender system and do the implementation into a trust-aware recommender system. The use of trust is expected to overcome the cold start user problem, data sparsity, and attack mentioned above. This final project analyze the accuracy and the number of predicted rating provided by trust-aware collaborative filtering and compare them with those provided by pure collaborative filtering.Using trust in recommender system can increase the accuracy and number of predictions. MAE and coverage resulted from trust-aware recommender system is better compared to pure collaborative filtering. Maximal propagation distance chosen was 3 because it can give MAE and coverage which are relatively better than other propagation.Keyword: recommender system, collaborative filtering, trust, trust-aware, cold start user, sparsit