AI News, Speech-Based, Natural Language Conversational Recommender Systems

Speech-Based, Natural Language Conversational Recommender Systems

SpeechRec, I wanted to build on the idea, that spoken language carries more semantic information than traditional user interfaces typically allow to express, by integrating paralingual features in the recommendation strategy, to give proportionally more weight to requirements that are said in a more forceful manner.

The ReComment prototype recommended compact digital cameras, which turned out to be a problem domain where a user’s requirements are fairly predictable, and finding a fitting product is arguably easy for the majority of users.

that the system would understand complex input, and instead defaulted to very simple, command-like sentences, which, in turn, carried less information about the user’s true, hidden preferences.

The speech recognition system was realized with the open-source speech recognition solution Simon, using a custom, domain-specific speech model that was especially adapted to the pervasive Styrian dialect.

sales dialog, did not hesitate to also talk about subjective attributes (e.g., “I want a device that looks good”), SpeechRec’s database additionally included sentiment towards 41 distinct aspects, sourced through automatic sentiment analysis from thousands of customer reviews.

The conducted empirical study showed, that the nuanced user input extracted from the natural language processing and paralingual analysis enabled SpeechRec to find better fitting products significantly quicker than when a traditional, knowledge-based recommender system.

Further comparison showed that the full version of SpeechRec described above also substantially outperformed a restricted version of itself, which was configured to not act on the extracted lexical and paralingual nuances, confirming that the richer user model facilitated by spoken natural language interaction is a major contribution toward the increase in recommendation performance of SpeechRec.