scholarly journals Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

2020 ◽  
Vol 10 (20) ◽  
pp. 7245
Author(s):  
Xiaofeng Liao ◽  
Xiangjun Li ◽  
Qingyong Xu ◽  
Hu Wu ◽  
Yongji Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Preferences ◽  
Ant Colony ◽  
Training Data ◽  
Practical Recommendation ◽  
Preference Change ◽  
Change Over Time ◽  
Unique Type ◽  
Over Time

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

Task Allocation in Case-Based Recommender Systems

2011 ◽  
pp. 268-279 ◽  
Author(s):  
Fabiana Lorenzi ◽  
Daniela Scherer dos Santos ◽  
Denise de Oliveira ◽  
Ana L.C. Bazzan
Keyword(s):  
Swarm Intelligence ◽  
Recommender Systems ◽  
Recommender System ◽  
Task Allocation ◽  
User Preferences ◽  
Cooperative Agents ◽  
Over Time ◽  
Case Based

Case-based recommender systems can learn about user preferences over time and automatically suggest products that fit these preferences. In this chapter, we present such a system, called CASIS. In CASIS, we combined the use of swarm intelligence in the task allocation among cooperative agents applied to a case-based recommender system to help the user to plan a trip.

scholarly journals A Hybrid Deep Collaborative Filtering Approach for Recommender Systems

2021 ◽  
Author(s):  
Kirubahari R ◽  
Miruna Joe Amali S
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Cold Start ◽  
Singular Value ◽  
User Preferences ◽  
Boltzmann Machine ◽  
Latent Features ◽  
Value Decomposition ◽  
Cold Start Problem

Abstract Recommender Systems (RS) help the users by showing better products and relevant items efficiently based on their likings and historical interactions with other users and items. Collaborative filtering is one of the most powerful technique of recommender system and provides personalized recommendation for users by prediction rating approach. Many Recommender Systems generally model only based on user implicit feedback, though it is too challenging to build RS. Conventional Collaborative Filtering (CF) techniques such as matrix decomposition, which is a linear combination of user rating for an item with latent features of user preferences, but have limited learning capacity. Additionally, it has been suffering from data sparsity and cold start problem due to insufficient data. In order to overcome these problems, an integration of conventional collaborative filtering with deep neural networks is proposed. A Weighted Parallel Deep Hybrid Collaborative Filtering based on Singular Value Decomposition (SVD) and Restricted Boltzmann Machine (RBM) is proposed for significant improvement. In this approach a user-item relationship matrix with explicit ratings is constructed. The user - item matrix is integrated to Singular Value Decomposition (SVD) that decomposes the matrix into the best lower rank approximation of the original matrix. Secondly the user-item matrix is embedded into deep neural network model called Restricted Boltzmann Machine (RBM) for learning latent features of user- item matrix to predict user preferences. Thus, the Weighted Parallel Deep Hybrid RS uses additional attributes of user - item matrix to alleviate the cold start problem. The proposed method is verified using two different movie lens datasets namely, MovieLens 100K and MovieLens of 1M and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate better prediction compared to other techniques in terms of accuracy.

scholarly journals Improve the Performance of Advice Systems Based on Cooperative Liquidation Using Trust Relationships

2019 ◽  
Vol 27 (1) ◽  
pp. 87-106
Author(s):  
EEva Diab Hriekes ◽  
Yosser AlSayed Souleiman AlAtassi
Keyword(s):  
Social Context ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
User Preferences ◽  
User Interactions ◽  
Trust Relationships ◽  
Recommendation Accuracy ◽  
User Reputation ◽  
User Ratings

Recommender systems are one of the recent inventions to deal with information overload problem and provide users with personalized recommendations that may be of their interests. Collaborative filtering is the most popular and widely used technique to build recommender systems and has been successfully employed in many applications. However, collaborative filtering suffers from several inherent issues that affect the recommendation accuracy such as: data sparsity and cold start problems caused by the lack of user ratings, so the recommendation results are often unsatisfactory. To address these problems, we propose a recommendation method called “MFGLT” that enhance the recommendation accuracy of collaborative filtering method using trust-based social networks by leveraging different  user's situations (as a trustor and as a trustee) in these networks to model user preferences. Specifically, we propose model-based method that uses matrix factorization technique and exploit both local social context represented by modeling explicit user interactions and implicit user interactions with other users, and also the global social context represented by the user reputation in the whole social network for making recommendations. Experimental results based on real-world dataset demonstrate that our approach gives better performance than the other trust-aware recommendation approaches, in terms of prediction accuracy.  

scholarly journals Ensemble Based Hybrid Recommender Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 826-833
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbor ◽  
Demographic Data ◽  
Weighted Average ◽  
Cold Start ◽  
User Preferences ◽  
Vector Method ◽  
Hybrid Recommender Systems ◽  
Hybrid Recommender

In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.

scholarly journals Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 439 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Vivian López Batista ◽  
M. Dolores Muñoz Vicente ◽  
Ángel Luis Sánchez Lázaro ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Behavior ◽  
Error Rates ◽  
User Preferences ◽  
Music Recommendation ◽  
Power Law Distribution ◽  
Long Tail ◽  
Music Recommender Systems ◽  
Structure Metrics

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

scholarly journals Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

2018 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Mohamed Hussein Abdi ◽  
George Onyango Okeyo ◽  
Ronald Waweru Mwangi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Preferences ◽  
Online Information ◽  
Context Aware ◽  
Preference Rating ◽  
Accessible Information ◽  
The Impact

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems. 

scholarly journals A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hongzhi Li ◽  
Dezhi Han
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Clustering Algorithm ◽  
Information Overload ◽  
User Feedback ◽  
User Preferences ◽  
User Preference ◽  
Logistic Regression Algorithm ◽  
Hybrid Recommendation ◽  
Time Aware

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

Utilizing Association Rules for Improving the Performance of Collaborative Filtering

2012 ◽  
Vol 3 (2) ◽  
pp. 14-28 ◽  
Author(s):  
Zainab Khanzadeh ◽  
Mehregan Mahdavi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Association Rules ◽  
Cold Start ◽  
User Preferences ◽  
Experimental Results ◽  
Internet Technology ◽  
Promising Technique ◽  
Novel Approach ◽  
Better Than

Internet technology has rapidly grown during the last decades. Presently, users are faced with a great amount of information and they need help to find appropriate items in the shortest possible time. Recommender systems were introduced to overcome this problem of overloaded information. They recommend items of interest to users based on their expressed preferences. Major e-commerce companies try to use this technology to increase their sales. Collaborative Filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user preferences. But one of the problems of Collaborative Filtering is cold-start. The authors provide a novel approach for solving this problem through using the attributes of items in order to recommend items to more people for improving e-business activities. The experimental results show that the proposed method performs better than existing methods in terms of the number of generated recommendations and their quality.

Web Service Usability Analysis Based on User Preferences

2018 ◽  
Vol 30 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Luiza Fabisiak
Keyword(s):  
Decision Support ◽  
Web Service ◽  
Original Method ◽  
User Preferences ◽  
Scoring Method ◽  
Change Over Time ◽  
Usability Analysis ◽  
Over Time

This article attempts to closely examine the users' preferences in the author's method of assessing the usability of websites. In particular, the issues bring a closer evaluation of websites by users. It sets rules for the accuracy of users' preferences on the basis of the scoring method. In the considered problem of assessing the usability of websites the methods of decision support, logs, and user preferences on the basis of the scoring method were used. It should be noted that websites and user preferences change over time and usually vary during the design from the pages already available on the network. Website aging forces companies to conduct a new study on the usability of websites. This article presents an original method for usability analysis based on users' preferences. The proposed method compares with other methods of usability of websites and conducts verification of this method on the basis of exemplary websites.

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