scholarly journals Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

2019 ◽  
Vol 9 (9) ◽  
pp. 1894 ◽  
Author(s):  
Zhi-Peng Zhang ◽  
Yasuo Kudo ◽  
Tetsuya Murai ◽  
Yong-Gong Ren
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Binary Relations ◽  
Excellent Performance ◽  
Satisfactory Accuracy ◽  
Available Information ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

2012 ◽  
Vol 457-458 ◽  
pp. 1544-1549
Author(s):  
Hang Yin ◽  
Gui Ran Chang ◽  
Xing Wei Wang
Keyword(s):  
Computation Time ◽  
Cold Start ◽  
Poor Quality ◽  
Recommendation Algorithm ◽  
Relevant Item ◽  
Item Attribute ◽  
Low Efficiency ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

scholarly journals CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering

2018 ◽  
Author(s):  
Quangui Zhang ◽  
Longbing Cao ◽  
Chengzhang Zhu ◽  
Zhiqiang Li ◽  
Jinguang Sun
Keyword(s):  
Collaborative Filtering ◽  
Performance Improvement ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
Cold Start ◽  
Large Datasets ◽  
Empirical Results ◽  
The Rich ◽  
Item Attributes

Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google’s Wide&Deep network.

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

2020 ◽  
Vol 149 ◽  
pp. 113248 ◽  
Author(s):  
Senthilselvan Natarajan ◽  
Subramaniyaswamy Vairavasundaram ◽  
Sivaramakrishnan Natarajan ◽  
Amir H. Gandomi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Open Data ◽  
Cold Start ◽  
Linked Open Data ◽  
Data Sparsity ◽  
Cold Start Problem

scholarly journals An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

2019 ◽  
Vol 3 (3) ◽  
pp. 39 ◽  
Author(s):  
Mahamudul Hasan ◽  
Falguni Roy
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommender System ◽  
Similarity Measures ◽  
Cold Start ◽  
Absolute Error ◽  
Prediction Algorithm ◽  
Rating Data ◽  
Asymmetric Correlation ◽  
Cold Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

P2PCF: A collaborative filtering based recommender system for peer to peer social networks

2021 ◽  
pp. 1-19
Author(s):  
Lyes Badis ◽  
Mourad Amad ◽  
Djamil Aïssani ◽  
Sofiane Abbar
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Peer To Peer ◽  
Public Concerns ◽  
Cold Start Problem ◽  
Filtering Approach

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.

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.

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