We consider the problem of personalized text-based music retrieval where users' history of preferences are taken into account in addition to their issued textual queries.Current retrieval methods mostly rely on songs meta-data. This limits the query vocabulary. Moreover, it is very costly to gather this information in large collections of music. Alternatively, we use music annotations retrieved from social tagging Websites such as last.fm and use them as textual descriptions of songs. Considering a user's profile and using preference patterns of music among all users, as in collaborative filtering approaches, can be useful in providing personalized and more satisfactory results. The main challenge is how to include both users' profiles and the songs meta-data in the retrieval model. In this paper, we propose a hierarchical probabilistic model that takes into account the users' preference history as well as tag co-occurrences in songs. Our model is an extension of LDA where topics are formed as joint clusterings of songs and tags. These topics capture the tag associations and user preferences and correspond to different music tastes. Each user's profile is represented as a distribution over topics which shows the user's interests in different types of music.We will explain how our model can be used for contextual retrieval. Our experimental results show significant improvement in retrieval when user profiles are taken into account.
QUILT (Query User Interface with Light Translations) is prototype implementation of a complete cross-language text retrieval system that takes English queries and produces English gloss translations of Spanish documents. The system indexes the Spanish documents in Spanish, but converts the English query into a Spanish equivalent set through a novel combination of lexical methods and parallel-corpus disambiguatinn. Similar methods are applied to the returned documento produce a simple translation that can be examined by non-Spanish speakers to gauge the relevance of the document to the original English query. The system integrates traditional, glossary-based machine txanslation technology with information retrieval approaches and demonstrates that relatively simple term substitution and disambiguation approaches can he viable for cross-language text retrieval. Components of QUILT have been used to build a CLTR interface to WWWbased search services.
Text processing has stimulated great interest over the last several years, prompted by technical advances in storage, searching, telecommunications, and user interfaces. The increasing generation of text causes problems in terms of storage and retrieval, and there are no signs of this trend abating in the future.
A lot of software troubles are happening in today's multivendor computing environments. While current computing environments have much functionality and flexibility, they are too complex and are vulnerable to software troubles. The rapid speed of change such as new product introductions and frequent version-ups makes the situation worse. Engineers working on software troubleshooting have to deal with various kinds of uncertainty. Problem descriptions conveyed from customers or other support engineers are often partial and incomplete.