Situation-aware thresholding for recommendation

Data processing: artificial intelligence – Knowledge processing system – Knowledge representation and reasoning technique

Reexamination Certificate

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Reexamination Certificate

active

08037007

ABSTRACT:
In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. The data is then clustered into clusters of data points. Then a centroid is determined for each of the clusters. A cluster similar to a current context of the user is selected by comparing a data point representing the current context of the user to one or more of the centroids. For each of one or more items, a threshold based on values for a plurality of the centroids with respect to the corresponding item, wherein a threshold is used to compare with centroid value of an item in a selected cluster to determine whether to recommend the item.

REFERENCES:
S. Yau, H. Liu, D. Huang, and Y. Yao, “Situation-aware Personalized Information Retrieval for Mobile Internet”, Proc. of the 27th Annual Int'l Comp. Soft. and Application Conference, 2007, pp. 1-6.
J A. Flanagan. “Unsupervised Clustering of Context Data and Learning User Requirements for A Mobile Device”, 5th Intl' and Interdisp. Conf. on Modeling and Using Context (Context-05), pp. 155-168,2005.
Jonathan L. Herlocker, Joseph A. Konstan, A1 Borchers, and John Riedl, “An Algorithmic Framework for Performing Collaborative Filtering”, in Proc. 25th Ann. Int. ACMSIGIR Con$ Res. Dev. In$ Retrieval, pp. 230-237,2002.
A. I. Schein, A. Popescul, and L. H. Ungar, “Methods and Metrics for Cold-Start Recommendations”, in Proc. 25th Ann. Int. ACMSIGIR Con$ Res. Dev. In$ Retrieval, pp. 253-260,2002.
Chen et al., “A Survey of Context-Aware Mobile Computing Research,” Dartmouth Computer Science Technical Report TR2000-381, Dartmouth College, 2000.

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