Abstract: In various embodiments, the present invention relates disparate objects based on user behavior, thus enabling search engines to provide more comprehensive and accurate results. According to various embodiments of the present invention, multiple kinds of interactions by users with multiple classes of objects can be analyzed. The result is that disparate classes of objects can be related. Derived relations between text and objects can be used to implement search-like functionality or to extend a conventional text retrieval system. In one embodiment, the present invention is used to improve search results and/or recommendations by employing a filtered co-occurrence matrix that provides a representation as to which queries tend to co-occur with the originally submitted query. By supplementing or replacing the original query with co-occurring queries, the system of the present invention is able to generate results that are more likely to be of interest.

Abstract: A recommendation system and method uses data structured according to an indicator-based recommendation paradigm. Items to be considered for recommendation are stored in a text retrieval system, along with associated meta-data such as title and description. To these conventional characteristics are added additional characteristics known as indicators which are derived from an analysis of the usage of the system by users. This indicator-based system provides a more robust recommendation system that is able to capture a greater depth and variety of real-world relationships among items, and is able to handle p-adic systems and systems with ternary or higher relations.