Journal Article10.1007/S11227-015-1518-5
An improved collaborative recommendation algorithm based on optimized user similarity
Hao Chen,Li Zhongkun,Wei Hu +2 more
37
TL;DR: Wang et al. as mentioned in this paper proposed an improved collaborative recommendation algorithm based on optimized user similarity, where a balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users.
read more
Abstract: There are lots of issues existing in traditional collaborative filtering recommendation, such as data scarcities, cold start, recommendation accuracy and timeliness. And how to improve the efficiency and quality of recommendation is a key problem in collaborative recommendation. In the traditional collaborative filtering algorithms, the rating scale of different users for all projects sometimes may be neglected while calculating the similarity. Some algorithms such as adjusted cosine similarity algorithm and the Pearson similarity algorithm are proposed to optimize this problem, but there still exists the problem that the single rating scale is different for the same project with different users. It may result in similar resultant vector results when the users have significant differences for the score vectors on a common set. The substantial presence of this kind of phenomena has a direct impact on the accuracy of user similarity calculation. Furthermore, it will affect the target user's predicted score accuracy. To solve the problem, an improved collaborative recommendation algorithm based on optimized user similarity is proposed. A balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users. Also, the most appropriate balance factor threshold can be obtained by experiments, a series of reasonable experiments to validate the effectiveness of the proposed algorithm based on the threshold. Experimental results show that the proposed improved collaborative filtering algorithm based on user similarity can significantly optimize the accuracy of user similarity and get better recommendation results.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A systematic review: machine learning based recommendation systems for e-learning
TL;DR: A taxonomy that accounts for components required to develop an effective recommendation system was developed and it was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components.
267
Efficient music recommender system using context graph and particle swarm
Rahul Katarya,Om Prakash Verma +1 more
TL;DR: A competent hybrid music recommender system (HMRS) is proposed, which works on context and collaborative approaches, and noticeably delivers the best recommendations regarding recall results when compared to existing methods.
69
An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests
TL;DR: A novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels is proposed.
57
Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning
TL;DR: A new recommendation approach based on social filtering and collaborative filtering for defining the best way in which the learner must learn, and recommend courses that better much theLearner’s profile and social content is proposed.
33
Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
TL;DR: An improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics, and results show that the proposed method has better performance than that of the state of the art.
20
References
GroupLens: an open architecture for collaborative filtering of netnews
Paul Resnick,Neophytos Iacovou,Mitesh Suchak,Peter Bergstrom,John Riedl +4 more
- 22 Oct 1994
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
6K
Using collaborative filtering to weave an information tapestry
TL;DR: Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser.
4.7K
Hybrid Recommender Systems: Survey and Experiments
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
4.4K
GroupLens: applying collaborative filtering to Usenet news
Joseph A. Konstan,Bradley N. Miller,David A. Maltz,Jonathan L. Herlocker,Lee R. Gordon,John Riedl +5 more
TL;DR: The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
2.8K
Relational learning via collective matrix factorization
Ajit P. Singh,Geoffrey J. Gordon +1 more
- 24 Aug 2008
TL;DR: This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.