AUTOMATIC TOPIC AND INTEREST BASED CONTENT RECOMMENDATION SYSTEM FOR MOBILE DEVICES

摘要

Disclosed are techniques for automatically performing topic and interest based content recommendation for mobile devices, which can help the users of mobile computing devices (e.g., smart phones) discover more of the information they want by delivering educated recommendations that are personalized to their interests, in ways that are more natural and comprehensible. More specifically, in some embodiments, techniques described herein include a topic and interest based content recommendation system, which may include several components, such as an automated recommendation server for content available on the Internet (e.g., webpages, applications, and events), and a mobile personalization application which may retrieve various types of data and user inputs from a mobile device, and may present content recommendation to the user (e.g., upon receiving such recommendation from the server).

主权项

1. A method for a computerized system to automatically recommend network-based content to a user of a mobile device regardless of whether the user has previously used the system, the method comprising:
before the user first uses the system, generating a most likely topic recommendation list by:
retrieving at least, from the mobile device, a predetermined number of web addresses of most recently visited webpages;categorizing, by utilizing a pattern matching module, the web addresses of most recently visited webpages into at least two categories, (1) search queries, and (2) general browsing histories;inferring likely topics based on the search queries, using a search keyword processing module, by:
for each search query: (1) extracting one or more search keywords from a given search query; (2) measuring a similarity between the one or more search keywords and a plurality of pre-indexed documents, wherein each of the pre-indexed documents has one or more known associated topics; and (3) assigning weighted similarity scores to the one or more known associated topics based on the measured similarity; andproducing a list of fixed topic user interest suggestions by combining the weighted similarity scores for each of the known associated topics;inferring likely topics based on the general browsing histories, using a browsing history processing module, by:
synthesizing a target document based on retrieving website information for each of the general browsing histories; andgenerating a probability distribution of topics of the target document; andselecting a predetermined percentage of topics, from the list of fixed topic user interest suggestions and from the probability distribution of topics of the target document, as the most likely topic recommendation list.