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).
Please help me write a proper abstract based on the patent claims. CLAIM: 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; and producing 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; and generating a probability distribution of topics of the target document; and selecting 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. 2. The method of claim 1, wherein inferring likely topics based on the search queries further comprises: extracting subphrases from the search queries that have been repeated a plurality of times in a recent period of time; verifying, by using a search engine, that the extracted subphrases produce document matches that have matching scores exceeding a minimum threshold; and producing a list of free form keyword user interest suggestions based on the extracted subphrases that are verified. 3. The method of claim 2, wherein selecting the predetermined percentage of topics is further based on the list of free form keyword user interest suggestions. 4. The method of claim 1, wherein inferring likely topics based on the search queries further comprises: determining that an associated topic with the highest weighted similarity score is a most likely topic for the given search query. 5. The method of claim 1, wherein inferring likely topics based on the search queries further comprises: discarding one or more of the pre-indexed documents if the similarity does not exceed a predetermined similarity threshold score. 6. The method of claim 5, wherein the subphrases include one or more of: an individual word, and a combination of multiple words. 7. The method of claim 1, wherein synthesizing a target document comprises: retrieving website title information for each of the general browsing histories; processing the website title information to remove extraneous information; and combining remaining information into the target document. 8. The method of claim 1, further comprising: before normal operations, training the search keyword processing module by: generating a corpus of documents related to each of a plurality of fixed topic model topics by utilizing a search engine and a known-good set of keywords for each of the plurality of fixed topic model topics; transforming each document in the corpus of documents by stripping stop words and mapping into a word-document co-occurrence matrix; converting the word-document co-occurrence matrix into a globally weighted term frequency-inverse document frequency (TF-IDF) matrix; converting the globally weighted term TF-IDF matrix into a matrix similarity index model, whereby the matrix similarity index model enables indexing from keywords to most similar documents; and storing the matrix similarity index model. 9. The method of claim 1, wherein generating a probability distribution of topics of the target document comprises: performing a term frequency-inverse document frequency (TF-IDF) based transformation to the target document; performing a K-Best feature selection on the transformed target document to select one or more document features, wherein the selection is based on a chi-squared goodness of fit metric; and based on the selected one or more document features, generating the probability distribution of topics of the target document by using a multinominal naïve Bayes classifier that is configured to output a full probability distribution of classifications. 10. The method of claim 1, further comprising: retrieving, from the mobile device, webpage bookmark data, wherein generating the most likely topic recommendation list is further based on the webpage bookmark data. 11. The method of claim 1, further comprising: retrieving, from the mobile device, application install data, wherein generating the most likely topic recommendation list is further based on the application install data. 12. The method of claim 1, further comprising: during the use of the system, retrieving, from the mobile device, application usage data and a set of topics specified by the user; and constructing a frequent pattern (FP) tree using an iterative topic inference engine based on (1) the most likely topic recommendation list generated before the user first uses the system, (2) the application usage data, and (3) the set of topics specified by the user, wherein the iterative topic inference engine is adapted to implement a variant of an FP-Growth association rule mining algorithm where each topic in a topic model is treated as an item; extracting a list of frequent itemsets from the FP tree; and generating a candidate ruleset by passing each frequent itemset into a candidate rule generator that implements an Apriori algorithm; and applying the candidate ruleset to produce a prediction of most likely followed topics for the user. 13. The method of 12, further comprising: updating the most likely topic recommendation list based on the prediction. 14. The method of claim 12, wherein only rules that exceed a predetermined confidence threshold are kept in the candidate ruleset. 15. The method of claim 12, further comprising: modulating the ruleset to increase rule confidence so as to capture known, hierarchical topic relationships. 16. A computerized system configured to automatically recommend network-based content to a user of a mobile device regardless of whether the user has previously used the system, the system comprising a processor and a memory storing a plurality of instructions which, when executed by the processor, causes the processor to perform a 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; and producing 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; and generating a probability distribution of topics of the target document; and selecting 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. 17. The system of claim 16, wherein inferring likely topics based on the search queries further comprises: extracting subphrases from the search queries that have been repeated a plurality of times in a recent period of time; verifying, by using a search engine, that the extracted subphrases produce document matches that have matching scores exceeding a minimum threshold; and producing a list of free form keyword user interest suggestions based on the extracted subphrases that are verified. 18. The system of claim 17, wherein selecting the predetermined percentage of topics is further based on the list of free form keyword user interest suggestions. 19. The system of claim 16, wherein inferring likely topics based on the search queries further comprises: determining that an associated topic with the highest weighted similarity score as a most likely topic for the given search query. 20. The system of claim 16, wherein inferring likely topics based on the search queries further comprises: discarding one or more of the pre-indexed documents if the similarity does not exceed a predetermined similarity threshold score. 21. The system of claim 20, wherein the subphrases include one or more of: an individual words, and a combination of multiple words. 22. The system of claim 16, wherein synthesizing a target document comprises: retrieving website title information for each of the general browsing histories; processing the website title information to remove extraneous information; and combining remaining information into the target document. 23. The system of claim 16, further comprising: before normal operations, training the search keyword processing module by: generating a corpus of documents related to each of a plurality of fixed topic model topics by utilizing a search engine and a known-good set of keywords for each of the plurality of fixed topic model topics; transforming each document in the corpus of documents by stripping stop words and mapping into a word-document co-occurrence matrix; converting the word-document co-occurrence matrix into a globally weighted term frequency-inverse document frequency (TF-IDF) matrix; converting the globally weighted term TF-IDF matrix into a matrix similarity index model, whereby the matrix similarity index model enables indexing from keywords to most similar documents; and storing the matrix similarity index model. 24. The system of claim 16, wherein generating a probability distribution of topics of the target document comprises: performing a term frequency-inverse document frequency (TF-IDF) based transformation to the target document; performing a K-Best feature selection on the transformed target document to select one or more document features, wherein the selection is based on a chi-squared goodness of fit metric; and based on the selected one or more document features, generating the probability distribution of topics of the target document by using a multinominal naïve Bayes classifier that is configured to output a full probability distribution of classifications. 25. The system of claim 16, wherein the method further comprises: retrieving, from the mobile device, webpage bookmark data, wherein generating the most likely topic recommendation list is further based on the webpage bookmark data. 26. The system of claim 16, wherein the method further comprises: retrieving, from the mobile device, application install data, wherein generating the most likely topic recommendation list is further based on the application install data. 27. The system of claim 16, wherein the method further comprises: during the use of the system, retrieving, from the mobile device, application usage data and a set of topics specified by the user; and constructing a frequent pattern (FP) tree using an iterative topic inference engine based on (1) the most likely topic recommendation list generated before the user first uses the system, (2) the application usage data, and (3) the set of topics specified by the user, wherein the iterative topic inference engine is adapted to implement a variant of an FP-Growth association rule mining algorithm where each topic in a topic model is treated as an item; extracting a list of frequent itemsets from the FP tree; and generating a candidate ruleset by passing each frequent itemset into a candidate rule generator that implements an Apriori algorithm; and applying the candidate ruleset to produce a prediction of most likely followed topics for the user. 28. The system of 27, wherein the method further comprises: updating the most likely topic recommendation list based on the prediction. 29. The system of claim 27, wherein only rules that exceed a predetermined confidence threshold are kept in the candidate ruleset. 30. The system of claim 27, wherein the method further comprises: modulating the ruleset to increase rule confidence so as to capture known, hierarchical topic relationships.