%0 Conference Paper %1 Carmel:2009:PSS:1645953.1646109 %A Carmel, David %A Zwerdling, Naama %A Guy, Ido %A Ofek-Koifman, Shila %A Har'el, Nadav %A Ronen, Inbal %A Uziel, Erel %A Yogev, Sivan %A Chernov, Sergey %B Proceedings of the 18th ACM conference on Information and knowledge management %C New York, NY, USA %D 2009 %I ACM %K Personalized basedsocial network search social %P 1227--1236 %R 10.1145/1645953.1646109 %T Personalized social search based on the user's social network %U http://doi.acm.org/10.1145/1645953.1646109 %X This work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people "similar" to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user's related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy. %@ 978-1-60558-512-3

@inproceedings{Carmel:2009:PSS:1645953.1646109, abstract = {This work investigates personalized social search based on the user's social relations -- search results are re-ranked according to their relations with individuals in the user's social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people "similar" to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user's related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy.}, acmid = {1646109}, added-at = {2013-07-01T16:41:07.000+0200}, address = {New York, NY, USA}, author = {Carmel, David and Zwerdling, Naama and Guy, Ido and Ofek-Koifman, Shila and Har'el, Nadav and Ronen, Inbal and Uziel, Erel and Yogev, Sivan and Chernov, Sergey}, biburl = {https://www.bibsonomy.org/bibtex/24b085bfba33ab13644acb2dc104a414e/griesbau}, booktitle = {Proceedings of the 18th ACM conference on Information and knowledge management}, description = {Personalized social search based on the user's social network}, doi = {10.1145/1645953.1646109}, interhash = {c44037e3bc421bd49f8984959ebb7675}, intrahash = {4b085bfba33ab13644acb2dc104a414e}, isbn = {978-1-60558-512-3}, keywords = {Personalized basedsocial network search social}, location = {Hong Kong, China}, numpages = {10}, pages = {1227--1236}, publisher = {ACM}, series = {CIKM '09}, timestamp = {2013-07-01T16:41:07.000+0200}, title = {Personalized social search based on the user's social network}, url = {http://doi.acm.org/10.1145/1645953.1646109}, year = 2009 }