Abstract

Collaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the similarity between users based on the set of their co-rated items. However, similarities are commonly computed without taking the popularity of the set of two users' co-rated items into consideration, e.g. an item rated by very many users should have less impact on the similarity measure, and analogously an item rated by few should have a larger impact on the similarity score of two users. In this paper, we investigate the effects of common weighting schemes on different types of users, i.e. new users with few ratings (so-called cold start users), post cold start users, and power users. Empirical studies over two datasets have shown in which of these cases weighting schemes are beneficial in terms of recommendation quality.

@inproceedings{Said:2012:AWS,
 author = {Said, Alan and Jain, Brijnesh J. and Albayrak, Sahin},
 title = {Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users},
 booktitle = {Proceedings of the 27th Annual ACM Symposium on Applied Computing},
 series = {SAC '12},
 year = {2012},
 isbn = {978-1-4503-0857-1},
 location = {Trento, Italy},
 pages = {2035--2040},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/2245276.2232114},
 doi = {10.1145/2245276.2232114},
 acmid = {2232114},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {cold start, collaborative filtering, diversity, item weighting scheme, popularity, recommender systems, similarity metrics, user modeling},
}