Connectedness of users–items networks and recommender systems

Gharibshah, Joobin; Jalili, Mahdi

doi:10.1016/j.amc.2014.06.024

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…In [38] it has been demonstrate that a user can constructs his/her social connections with someone who has similar tastes. Gharibshah and Jalili have conducted a study of the relation between recommender systems and connectedness of users-items bipartite interaction network [39] . Guo et al proposed a method which merged the ratings of users' trusted neighbors with the other information sources to identify their preferences [40] .…”

Section: Trust-aware Recommendationsmentioning

confidence: 99%

A Temporal and Social Network-based Recommender using Graph Clustering

Ahmed¹,

Hamakarim

Kakarash³

2022

PJBAS

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Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users' community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.

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Section: Trust-aware Recommendationsmentioning

confidence: 99%

A Temporal and Social Network-based Recommender using Graph Clustering

Ahmed¹,

Hamakarim

Kakarash³

2022

PJBAS

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“…The Laplacian matrix can measure the reachability of nodes in graph models. Since the distance between nodes is calculated in bipartite graphs, they can be transformed into the similarity between nodes [17]. Typical algorithms include mean similarity [18], and subsequent research has shown the upper and lower bounds of bipartite approximations [19].…”

Section: Literature Review 21 Graph Modelmentioning

confidence: 99%

A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

Wang¹,

Chen²

2020

Banking and Finance

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It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns.

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“…Incorporating the latent factors associated with users has been proven to be very useful to design effective algorithms in ranking and recommendation systems [8,28,29]. These works are based on the idea of factorizing a matrix to linearly reduce the dimensionality of the problem and extract some commonalities that may be implicit in the data under analysis.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods

et al. 2020

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Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.

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Connectedness of users–items networks and recommender systems

Cited by 13 publications

References 36 publications

A Temporal and Social Network-based Recommender using Graph Clustering

A Temporal and Social Network-based Recommender using Graph Clustering

A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods

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