Colin Cooper
ABSTRACT
A recommender system uses information about known associations between users and items to compute for a given user an ordered recommendation list of items which this user might be interested in acquiring. We consider ordering rules based on various parameters of random walks on the graph representing associations between users and items. We experimentally compare the quality of recommendations and the required computational resources of two approaches: (i) calculate the exact values of the relevant random walk parameters using matrix algebra; (ii) estimate these values by simulating random walks. In our experiments we include methods proposed by Fouss et al. and Gori and Pucci, method P3, which is based on the distribution of the random walk after three steps, and method P3a, which generalises P3. We show that the simple method P3 can outperform previous methods and method P3a can offer further improvements. We show that the time- and memory-efficiency of direct simulation of random walks allows application of these methods to large datasets. We use in our experiments the three MovieLens datasets.
- Anonymous ratings data from the Jester. http://goldberg.berkeley.edu/jester-data.Google Scholar
- Last.fm. http://www.last.fm/.Google Scholar
- Lg u oz store. http://ozstore.uplus.co.kr/.Google Scholar
- Movielens data sets. GroupLens Research Lab, Dept. Computer Science and Engineering, University of Minnesota. http://www.grouplens.org/node/73.Google Scholar
- D. Billsus and M. J. Pazzani. Learning collaborative information filters. In ICML, pp. 46--54, 1998. Google ScholarDigital Library
- N. Craswell and M. Szummer Random walks on the click graph. In SIGIR, pp. 239--246, 2007. Google ScholarDigital Library
- F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng., 19(3):355--369, 2007. Google ScholarDigital Library
- F. Fouss, A. Pirotte, and M. Saerens. A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. In Web Intelligence, pp. 550--556, 2005. Google ScholarDigital Library
- M. Gori and A. Pucci. Research paper recommender systems: A random-walk based approach. In Web Intelligence, pp. 778--781, 2006. Google ScholarDigital Library
- M. Gori and A. Pucci. Itemrank: A random-walk based scoring algorithm for recommender engines. In IJCAI, pp. 2766--2771, 2007. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, John, and T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22:5--53, 2004. Google ScholarDigital Library
- J. Kunegis and S. Schmidt. Collaborative filtering using electrical resistance network models. In Industrial Conf. on Data Mining, pp. 269--282, 2007. Google ScholarDigital Library
- S. Lee ,S. Song, M. Kahng, D. Lee, and S. Lee. Random walk based entity ranking on graph for multidimensional recommendation. In RecSys, pp. 93--100, 2011. Google ScholarDigital Library
- S. Lee, S. Park, M. Kahng, and S. Lee. Pathrank: a novel node ranking measure on a heterogeneous graph for recommender systems. In CIKM, pp. 1637--1641, 2012. Google ScholarDigital Library
- S. Lee, S. Park, M. Kahng, and S. goo Lee. Pathrank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems. Expert Syst. Appl., 40(2):684--697, 2013. Google ScholarDigital Library
- L. Lovász. Random walks on graphs: A survey. Bolyai Society Mathematical Studies, 2:353--397, 1996.Google Scholar
- P. Sarkar and A. W. Moore. A tractable approach to finding closest truncated-commute-time neighbors in large graphs. In UAI, pp. 335--343. AUAI, 2007.Google Scholar
- A. P. Singh, A. Gunawardana, C. Meek, and A. C. Surendran. Recommendations using absorbing random walks. In NESCAI, 2007.Google Scholar
- L. Zhang, J. Xu, and C. Li. A random-walk based recommendation algorithm considering item categories. Neurocomputing, 120(0):391 -- 396, 2013.Google ScholarCross Ref
- L. Zhang, K. Zhang, and C. Li. A topical pagerank based algorithm for recommender systems. In SIGIR, pp. 713--714, 2008. Google ScholarDigital Library
Index Terms
- Random walks in recommender systems: exact computation and simulations
Recommendations
Collaborative filtering recommendations based on multi-factor random walks
AbstractUsing trust relationships can improve the accuracy of recommendation systems; however, it is affected by data sparsity. Random walks can harvest the behavioral relationships between users and compensate for data sparsity. However, existing random ...
A Recommendation Algorithm via Developed Random Walk Takes User's Preference and Item's Genres
ICISDM '17: Proceedings of the 2017 International Conference on Information System and Data MiningIt is difficult to quickly find the information that users are most interested in due to the increasing number and diversity of information. The traditional random walk algorithm only considers all the users' choice of items and ignores the user and ...
Diffusivity of a random walk on random walks
We consider a random walk Zn1,',ZnK+1∈ï K+1 with the constraint that each coordinate of the walk is at distance one from the following one. In this paper, we show that this random walk is slowed down by a variance factor ï K2=2K+2 with respect to the ...
Comments