Collaborative Filtering Recommender System: Comparative Survey on Cold-Start Issue

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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Cold-Start User-Based Weighted Collaborative Filtering for an Implicit Recommender System for Research Facilities

2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC) ◽

10.1109/cic.2018.00070 ◽

2018 ◽

Author(s):

Yogesh Kale ◽

Samantha E. Petrie ◽

Marwan Bikdash ◽

Michael D. Topal

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Cold Start ◽

Research Facilities

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An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Future Internet ◽

10.3390/fi11090182 ◽

2019 ◽

Vol 11 (9) ◽

pp. 182 ◽

Cited By ~ 1

Author(s):

Paul Sheridan ◽

Mikael Onsjö ◽

Claudia Becerra ◽

Sergio Jimenez ◽

George Dueñas

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

System Development ◽

Cold Start ◽

Star Trek ◽

User Preference ◽

Future Research ◽

Television Series ◽

K Nearest Neighbors

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

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

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/509 ◽

2018 ◽

Cited By ~ 9

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.

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Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

Jurnal Nasional Teknologi Terapan (JNTT) ◽

10.22146/jntt.44938 ◽

2019 ◽

Vol 2 (3) ◽

pp. 334

Author(s):

Imam Fahrurrozi ◽

Estu Muh Dwi Admoko ◽

Anang Susilo

Keyword(s):

Collaborative Filtering ◽

Semantic Similarity ◽

Recommender System ◽

Cold Start ◽

Performance Tests ◽

Online Commerce ◽

Filtering Method ◽

Similarity Method

Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.

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

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113248 ◽

2020 ◽

Vol 149 ◽

pp. 113248 ◽

Cited By ~ 7

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

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An Evaluation Framework of Trust Aware Recommender System

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.33.23472 ◽

2018 ◽

Vol 7 (4.33) ◽

pp. 5

Author(s):

S. Masrom ◽

N. Khairuddin ◽

A. Abdul Rahman ◽

A. Azizan ◽

A. S.A. Rahman

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Cold Start ◽

Evaluation Framework ◽

Research Progress ◽

Comparison Study ◽

Common Group ◽

Common Problems ◽

Content Based Filtering

To date, there exists a variety of prediction approaches have been used in recommender systems. Among the widely known approaches are Content Based Filtering (CBF) and Collaborative Filtering (CF). Based on literatures, CF with users rating element has been widely used but the approach faced two common problems namely cold start and sparsity. As an alternative, Trust Aware Recommender Systems (TARS) for the CF based users rating has been introduced. The research progress on TARS improvement is found to be rapidly progressing but lacking in the algorithm evaluation has been started to appear. Many researchers that introduced their new TARS approach provides different evaluation of users’ views for the TARS performances. As a result, the performances of different TARS from different publications are not comparable and difficult to be analyzed. Therefore, this paper is written with objective to provide common group of the users’ views based on trusted users in TARS. Then, this paper demonstrates a comparison study between different TARS techniques with the identified common groups by means of the accuracy error, rating and users coverage. The results therefore provide a relative comparison between different TARS.

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

Applied Sciences ◽

10.3390/app9091894 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1894 ◽

Cited By ~ 2

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.

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

Big Data and Cognitive Computing ◽

10.3390/bdcc3030039 ◽

2019 ◽

Vol 3 (3) ◽

pp. 39 ◽

Cited By ~ 2

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.

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