scholarly journals An efficient hybrid recommendation model based on collaborative filtering recommender systems

2021 ◽  
Author(s):  
Mohammed Fadhel Aljunid ◽  
Manjaiah Doddaghatta Huchaiah
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Model Based ◽  
Hybrid Recommendation

scholarly journals Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41782-41798 ◽  
Author(s):  
Santiago Alonso ◽  
Jesus Bobadilla ◽  
Fernando Ortega ◽  
Ricardo Moya
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Model Based ◽  
Robust Model

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

scholarly journals A Survey of Collaborative Filtering Techniques

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoyuan Su ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
State Of The Art ◽  
Predictive Performance ◽  
The State ◽  
Data Sparsity ◽  
Research And Practice ◽  
Model Based ◽  
Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

scholarly journals An Efficient and Intelligent Recommender System for Mobile Platform

2018 ◽  
Vol 37 (4) ◽  
pp. 463-480
Author(s):  
Muhammad Jabbar ◽  
Qaisar Javaid ◽  
Muhammad Arif ◽  
Asim Munir ◽  
Ali Javed
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Past History ◽  
Subjective Evaluation ◽  
Mobile Platform ◽  
Value Addition ◽  
Hybrid Scheme ◽  
Hybrid Approaches ◽  
User Ratings ◽  
Hybrid Recommendation

Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

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.

scholarly journals Model-Based Learning from Preference Data

2019 ◽  
Vol 6 (1) ◽  
pp. 329-354 ◽  
Author(s):  
Qinghua Liu ◽  
Marta Crispino ◽  
Ida Scheel ◽  
Valeria Vitelli ◽  
Arnoldo Frigessi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Rank Aggregation ◽  
Incomplete Preferences ◽  
Preference Learning ◽  
Preference Data ◽  
Individual Ranking ◽  
Model Based ◽  
Different Types

Preference data occur when assessors express comparative opinions about a set of items, by rating, ranking, pair comparing, liking, or clicking. The purpose of preference learning is to ( a) infer on the shared consensus preference of a group of users, sometimes called rank aggregation, or ( b) estimate for each user her individual ranking of the items, when the user indicates only incomplete preferences; the latter is an important part of recommender systems. We provide an overview of probabilistic approaches to preference learning, including the Mallows, Plackett–Luce, and Bradley–Terry models and collaborative filtering, and some of their variations. We illustrate, compare, and discuss the use of these methods by means of an experiment in which assessors rank potatoes, and with a simulation. The purpose of this article is not to recommend the use of one best method but to present a palette of different possibilities for different questions and different types of data.

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