By Hongzhi Yin, Bin Cui
This ebook covers the foremost basics of and the newest learn on next-generation spatio-temporal suggestion structures in social media. It starts off by means of describing the rising features of social media within the period of cellular web, and explores the constraints to be present in present recommender innovations. The booklet for this reason offers a sequence of latent-class consumer types to simulate clients’ behaviors in decision-making strategies, which successfully conquer the demanding situations coming up from temporal dynamics of clients’ behaviors, consumer curiosity glide over nation-states, facts sparsity and chilly commence. Based on those good designed consumer versions, the ebook develops powerful multi-dimensional index constructions corresponding to Metric-Tree, and proposes effective top-k retrieval algorithms to speed up the method of on-line suggestion and aid real-time advice. additionally, it deals methodologies and strategies for comparing either the effectiveness and potency of spatio-temporal suggestion platforms in social media. The booklet will attract a huge readership, from researchers and builders to undergraduate and graduate scholars.
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Extra resources for Spatio-Temporal Recommendation in Social Media (SpringerBriefs in Computer Science)
In: SIGIR, pp. 223–232 (2013) 4. : Social-network analysis using topic models. In: SIGIR, pp. 565–574 (2012) References 39 5. : Elements of Information Theory. Wiley-Interscience, New Jersey (1991) 6. : Google news personalization: scalable online collaborative filtering. In: WWW, pp. 271–280 (2007) 7. : Mymedialite: a free recommender system library. In: RecSys, pp. 305–308 (2011) 8. : Probabilistic latent semantic analysis. In: UAI (1999) 9. : Information contagion: an empirical study of the spread of news on digg and twitter social networks.
We collected 201,663 users and their tagging behaviors during the period Feb. 2008–Dec. 2009. The dataset contains 2,828,304 tags. Topics on technology and electronics account for about half of all web pages. Most of the other web pages are about breaking news with strong temporal features. 2 Comparisons The temporal context-aware mixture model (TCAM) was outlined in Sect. 2. TCAM can be enhanced by the item-weighting scheme, which leads to a weighted TCAM, called WTCAM. We compare them with four categories of competitor approaches.
1 Datasets Our experiments are conducted on four real datasets: Digg, MovieLens, Douban Movie, and Delicious. 2. Only the implicit feedback data can be available in Digg and Delicious datasets, so we compute the cell value C[u, t, v] for these two datasets according to the frequency/number of the interaction between user u and item v at time t. 2 Basic statistics of the four datasets Digg MovieLens # of users # of items # of ratings time span(year) 139,409 3,553 3,018,197 2009–2010 71,567 10,681 10,000,054 1998–2009 Douban movie Delicious 33,561 87,081 5,257,665 2005–2010 201,663 2,828,304 36,966,661 2008–2009 Movie and MovieLens datasets, the explicit feedback information is available.