Abstract
Recommender systems can provide users with relevant items based on each user’s preferences. However, in the domain of mobile applications (apps), existing recommender systems merely recommend apps that users have experienced (rated, commented, or downloaded) since this type of information indicates each user’s preference for the apps. Unfortunately, this prunes the apps which are releavnt but are not featured in the recommendation lists since users have never experienced them. Motivated by this phenomenon, our work proposes a method for recommending serendipitous apps using graph-based techniques. Our approach can recommend apps even if users do not specify their preferences. In addition, our approach can discover apps that are highly diverse. Experimental results show that our approach can recommend highly novel apps and reduce over-personalization in a recommendation list.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adamopoulos, P., Tuzhilin, A.: On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected. Technical Report CBA-13-03, Stern School of Business, New York University (2013)
Adomavicius, G., Kwon, Y.: Maximizing Aggregate Recommendation Diversity: A Graph-Theoretic Approach. In: Proc. of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011), pp. 3–10 (2011)
Aggarwal, C.C., Wolf, J.L., Wu, K.-L., Yu, P.S.: Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1999), pp. 201–212 (1999)
Andre, P., Schraefel, M.C., Teevan, J., Dumais, S.T.: Discovery is Never by Chance: Designing for (Un)Serendipity. In: Proc. of the 7th SIGCHI Conference on Creativity and Cognition (C&C 2009), pp. 305–314 (2009)
Andre, P., Teevan, J., Dumais, S.T.: From X-Rays to Silly Putty via Uranus: Serendipity and its Role in Web Search. In: Proc. of the 27th International Conference on Human Factors in Computing Systems (CHI 2009), pp. 2033–2036 (2009)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In: Proc. of the 15th National Conference on Artificial Intelligence (AAAI 1998), pp. 714–720 (1998)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conference on Uncertanity in Artificial Intelligence (UAI 1998), pp. 43–52 (1998)
Costa-Montenegro, E., Barragáns-Martínez, A.B., Rey-López, M.: Which App? A Recommender System of Applications in Markets: Implementation of the Service for Monitoring Users’ Interaction. Expert Systems with Applications: An International Journal 39(10), 9367–9375 (2012)
Datta, A., Dutta, K., Kajanan, S., Pervin, N.: Mobilewalla: A Mobile Application Search Engine. Mobile Computing, Applications, and Services 95(5), 172–187 (2012)
Davidsson, C., Moritz, S.: Utilizing Implicit Feedback and Context to Recommend Mobile Applications from First Use. In: Proc. of the 2011 Workshop on Context-awareness in Retrieval and Recommendation (CaRR 2011), pp. 19–22 (2011)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.B.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)
Kawamae, N.: Serendipitous Recommendations via Innovators. In: Proc. of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 218–225 (2010)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal Diversity in Recommender Systems. In: Proc. of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 210–217 (2010)
Lin, J., Sugiyama, K., Kan, M.-Y., Chua, T.-S.: Addressing Cold-Start in App Recommendation: Latent User Models Constructed from Twitter Followers. In: Proc. of the 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013), pp. 283–292 (2013)
Nakatsuji, M., Fujiwara, Y., Tanaka, A., Uchiyama, T., Fujimura, K., Ishida, T.: Classical Music for Rock Fans?: Novel Recommendations for Expanding User Interests. In: Proc. of the 19th International Conference on Information and Knowledge Management (CIKM 2010), pp. 949–958 (2010)
Resnick, P., Iacovou, N., Suchak, M., Bergstorm, J.R.P.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of the ACM 1994 Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186 (1994)
Ricci, F., Shapira, L., Kantor, B.: Recommender Systems Handbook. Springer (2011)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill (1983)
Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: Proc. of the 10th International World Wide Web Conference (WWW10), pp. 285–295 (2001)
Sarwar, B.M., Karypis, G., Konstan, J.A.: Analysis of Recommendation Algorithms for E-commerce. In: Proc. of the 2nd ACM Conference on Electronic Commerce (EC 2000), pp. 158–167 (2000)
Sugiyama, K., Kan, M.-Y.: Serendipitous Recommendation for Scholarly Papers Considering Relations Among Researchers. In: Proc. of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL 2011), pp. 307–310 (2011)
Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., Venkataraman, S.: Identifying Diverse Usage Behaviors of Smartphone Apps. In: Proc. of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference (IMC 2011), pp. 329–344 (2011)
Yan, B., Chen, G.: AppJoy: Personalized Mobile Application Discovery. In: Proc. of the 9th International Conference on Mobile Systems, Applications and Services (MobiSys 2011), pp. 113–126 (2011)
Yin, P., Luo, P., Lee, W.-C., Wang, M.: App Recommendation: A Contest between Satisfaction and Temptation. In: Proc. of the 6th International Conference on Web Search and Data Mining (WSDM 2013), pp. 395–404 (2013)
Zhang, M., Hurley, N.: Avoiding Monotony: Improving the Diversity of Recommendations. In: Proc. of the 2008 ACM Conference on Recommender Systems (RecSys 2008), pp. 123–130.
Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving Recommendation Lists Through Topic Diversification. In: Proc. of the 14th International World Wide Web Conference (WWW 2005), pp. 22–32 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhandari, U., Sugiyama, K., Datta, A., Jindal, R. (2013). Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-45068-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45067-9
Online ISBN: 978-3-642-45068-6
eBook Packages: Computer ScienceComputer Science (R0)