Applying Small Sample Test Statistics for Behavior-based Recommendations
This contribution reports on the development of small sample test statistics for identifiying recommendations in market baskets. The main application is to lessen the cold start problem of behavior-based recommender systems by faster generating quality recommendations out of the first small samples of user behavior. The derived methods are applied in the area of library networks but are generally applicable in any consumer store setting. Analysis of market basket size at different organisational levels of German research library networks reveals that at the highest network level market basket size is considerably smaller than at the university level. The overall data volume is considerably higher. These facts motivate the development of small sample tests for the identification of non-random sample patterns. As in repeat-purchase theory the independent stochastic processes are modelled. The small sample tests are based on modelling the choice-acts of a decision maker completely without preferences by a multinomial model and combinatorial enumeration over a series of increasing event spaces. A closed form of the counting process is derived.
KeywordsRecommender System Event Space Market Basket Cold Start Problem Integer Partition
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