Naïve filterbots for robust cold-start recommendations

Park, Seung-Taek; Pennock, David M.; Madani, Omid; Good, Nathan; DeCoste, Dennis

doi:10.1145/1150402.1150490

Cited by 133 publications

(80 citation statements)

References 23 publications

(28 reference statements)

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“…Depending on the hybridization approach different types of systems can be found [6]. There have been some works on using boosting algorithms for hybrid recommendations [7,8]. These works attempt to generate new synthetic ratings in order to improve recommendation quality.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents

Okaka¹,

Mwangi²,

Okeyo³

2016

IJCATR

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Abstract:Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users' access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents

Okaka¹,

Mwangi²,

Okeyo³

2016

IJCATR

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“…This dataset has been a popular tool for many researchers of recommender systems because of its high density (Park et al, 2006) and straightforward domain (i.e., movies). In our experiments we used the 100K dataset, which comprises 100K ratings of 943 users on 1682 movies.…”

Section: Usage Of Similaritiesmentioning

confidence: 99%

Caching Strategies for In-memory Neighborhood-based Recommender Systems

Dooms¹,

Pessemier²,

Martens

2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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Abstract:Neighborhood-based recommender systems rely greatly on calculated similarity values to match interesting items with users in online information systems. Because sometimes there are too many similarity values or available memory is limited it is not always possible to calculate and store all these values in advance. Sometimes only a subset can be stored and recalculations cannot be avoided. In this work we focus on caching systems that optimize this trade-off between memory requirements and computational redundancy in order to speed up the recommendation calculation process. We show that similarity values are not equally important and some are used considerably more than others during calculation. We devised a caching strategy (referred to as SMART-cache) that incorporates this usage frequency knowledge and compared it with a basic least recently used (LRU) caching mechanism. Results showed total execution time could be reduced by a factor of 5 using LRU for a cache storing only 0.2% of the total number of similarity values. The speedup of the SMART approach on the other hand was less affected by the order in which user-item pairs were calculated.

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“…However, because of large resource sets and the sparseness of rating data, collaborative filtering fails to solve the problems of cold start and others. Recently researches focus on creating virtual users to augment grading for items [5], explain new products with fuzzy natural language processing [6], or cluster users and apply collaborative filtering to clustered groups [7].…”

Section: Related Workmentioning

confidence: 99%

Personalized Paper Recommendation Based on User Historical Behavior

Wang

Liu

Dong

et al. 2012

Communications in Computer and Information Science

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Abstract. With the increasing of the amount of the scientific papers, it is very important and difficult for paper-sharing platforms to recommend related papers accurately for users. This paper tackles the problem by proposing a method that models user historical behavior. Through collecting the operations on scientific papers of online users and carrying on the detailed analysis, we build preference model for each user. The personalized recommendation model is constructed based on contentbased filtering model and statistical language model.. Experimental results show that users' historical behavior plays an important role in user preference modeling and the proposed method improves the final predication performance in the field of technical papers recommendation.

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Naïve filterbots for robust cold-start recommendations

Cited by 133 publications

References 23 publications

A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents

A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents

Caching Strategies for In-memory Neighborhood-based Recommender Systems

Personalized Paper Recommendation Based on User Historical Behavior

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