Finding item neighbors in item-based collaborative filtering by adding item content

Tiraweerakhajohn, C.; Pinngern, Ouen

doi:10.1109/icarcv.2004.1469313

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…In order to improve the accuracy of recommendation and resolve the data sparseness and "cold start" problems, item-based collaborative filtering algorithm made many improvements, Chaiwat et al enhance similarity between nearest neighbor items by considering the content of item [7].…”

Section: A Collaborative Filtering Algorithmmentioning

confidence: 99%

Improving item-based collaborative filtering recommendation system with tag

Hui

Zhao

2011

2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)

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Since market segmentation brings the need of personalized service and long tail phenomenon is continuously proven in the Internet applications, recommendation systems have been paid more and more attention. Item-based collaborative filtering algorithms as the one of most widely used and successful recommendation technology have been continuously improved. But traditional item-based collaborative filtering algorithms cannot solve the data sparseness and the "cold start" problems properly, and handle the over-reliance on the user rating information without consideration of the user's rating subjective factor. With the growing up of Web2.0, tag has been widely used, which allows users to define characteristics of objects from their own point of view. As a consequence, the interaction between the user and recommendation system is improved, and a new way of thinking to improve the quality of recommendation is provided as considering the view point of user in the recommendation. This paper uses tag-based method to calculate the similarity between users, and in the process of calculating item similarity, which makes use of TAG to calculate the similarity between the current user and each user in the candidate set to filter out users with different interest points, thereby it enhances the credibility of item similarity and guarantees the quality of recommendation quality as well. And based on mentioned above, the recommendation system framework is designed, meanwhile which facilitates further research.

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Section: A Collaborative Filtering Algorithmmentioning

confidence: 99%

Improving item-based collaborative filtering recommendation system with tag

Hui

Zhao

2011

2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC)

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“…Conventional recommender methods consisted of content-based approach, collaborative filtering approach, knowledge-based approach, utility-based approach and hybrid approach [13] [14][15]. Under its application in different domains, customers (C) and items (I) were mainly used as the entities of the recommender system.…”

Section: Literature Reviewmentioning

confidence: 99%

Mingling the Contextual Information in Improved Multidimensional Recommendation System

Husain¹

2011

IJC

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Recommender systems utilize the times of yore experiences and preferences of the target customers as a basis to proffer personalized recommendations for them as well as resolve the information overloading hitch. Personalized recommendation methods are primarily classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches have their own advantages, drawbacks and complementarities. Because conventional recommendation techniques don’t consider the contextual information, the real factor why a customer likes a specific product is unable to be understood. Therefore, in reality, it often causes a decrease in the accuracy of the recommendation results and also persuades the recommendation quality. In this paper, we propose the integrated contextual information as the foundation concept of multidimensional recommendation model and use the Online Analytical Processing (OLAP) ability of data warehousing to solve the contradicting tribulations among hierarchy ratings. This work hopes that by establishing additional user profiles and multidimensional analysis to find the key factors affecting user perceptions, it would increase the recommendation quality.

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“…It can discover the potential needs of users, and has been widely used due to its strong application value. Currently, collaborative filtering algorithms [1] can be divided into two main categories: memory-based [2][3][4][5] and model-based collaborative filtering [6][7][8], where memory-based collaborative filtering can be further divided into userbased collaborative filtering [2,3], item-based collaborative filtering [4,5], and the combination of the two collaborative filterings [9][10][11]. In memory-based collaborative filtering, the similarities between users and items are calculated based on the users' ratings on items to make recommendations.…”

Section: Introductionmentioning

confidence: 99%

Improved collaborative filtering algorithm using intuitionistic fuzzy C -means clustering and incorporating time factors

Zhang

Wang

2022

Preprint

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Collaborative filtering (CF) has been widely used to help users discover items of interest. Most of the existing CF methods collect preference information from other users to predict user’s ratings. However, the user’s interest will change over time, and the traditional collaborative filtering recommendation does not take the change of user’s interest into consideration. In addition, there are also problems of information overload and data sparseness in applications. In order to solve the problem of data sparseness and take the changes of user interests into account , we propose an improved collaborative filtering algorithm using intuitionistic fuzzy C-means clustering and incorporating time factors. Firstly, we introduce intuitionistic fuzzy C-means clustering to complete the classification of items; secondly, the above classification results are combined with the user-item score matrix to obtain the user-type preference matrix; thirdly, the joint similarities are used to obtain the nearest neighbors of the target user; finally, the changes of user interest over time are taken into account when predicting the target user’s rating on unrated items, top-k items are selected as recommendation results. To verify the effectiveness of the algorithm, experiments are conducted on Movie Lens and Booking-Crossing datasets. The Improved collaborative filtering algorithm using intuitionistic fuzzy C-means clustering results show that compared with the traditional recommendation algorithms , the algorithm proposed in this paper can effectively solve data sparseness and improve the quality and accuracy of recommendation.

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Finding item neighbors in item-based collaborative filtering by adding item content

Cited by 9 publications

References 6 publications

Improving item-based collaborative filtering recommendation system with tag

Improving item-based collaborative filtering recommendation system with tag

Mingling the Contextual Information in Improved Multidimensional Recommendation System

Improved collaborative filtering algorithm using intuitionistic fuzzy C -means clustering and incorporating time factors

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