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Connecting information need to recommendations

Connecting information need to recommendations

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A new article published by InfoSeekers Shawon Sarkar, Matt Mitsui, Jiqun Liu, and Chirag Shah in the Journal of Information Processing and Management (IP&M), shows how we could use behavioral signals from a user in a search episode to explicate their information need, their perceived problems, and the potential help they may need.

Here are some highlights.

  • The amount of time spent on previous search results could be an indicator of potential problems in articulation of needs into queries, perceiving useless results, and not getting useful sources in the following search stage in an information search process.
  • While performing social tasks, users mostly searched with an entirely new query, whereas, for cognitive and moderate to high complexity tasks, users used both new and substituted queries as well.
  • From usersโ€™ search behaviors, it is possible to predict the potential problem that they are going to face in the future.
  • Userโ€™s search behaviors can map an information searcherโ€™s situational need, along with his/her perception of barriers and helps in different stages of an information search process.
  • By combining perceived problem(s) and search behavioral features, it is possible to infer usersโ€™ needed help(s) in search with a certain level of accuracy (78%).

Read more about it at https://www.sciencedirect.com/science/article/pii/S0306457319300457

A new NSF grant for explainable recommendations

A new NSF grant for explainable recommendations

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Dr. Yongfeng Zhang from Rutgers University and Dr. Chirag Shah from University of Washington are recipients of a new grant from NSF (3 years, $500k) to work on explainable recommendations. It’s a step toward curing the “runaway AI”!

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910154

Recommendation systems are essential components of our daily life. Today, intelligent recommendation systems are used in many Web-based systems. These systems provide personalized information to help human decisions. Leading examples include e-commerce recommendations for everyday shopping, job recommendations for employment markets, and social recommendations to make people better connected. However, most recommendation systems merely suggest recommendations to users. They rarely tell users why such recommendations are provided. This is primarily due to the closed nature algorithms behind the systems that are difficult to explain. The lack of good explainability sacrifices transparency, effectiveness, persuasiveness, and trustworthiness of recommendation systems. This research will allow for personalized recommendations to be provided in more explainable manners, improving search performance and transparency. The research will benefit users in real systems through researchers? industry collaboration with e-commerce and social networks. New algorithms and datasets developed in the project will supplement courses in computer science and iSchool programs. Presentation of the work and demos will help to engage with wider audiences that are interested in computational research. Ultimately, the project will make it easier for humans to understand and trust the machine decisions.

This project will explore a new framework for explainable recommendation that involves both system designers and end users. The system designers will benefit from structured explanations that are generated for model diagnostics. The end users will benefit from receiving natural language explanations for various algorithmic decisions. This project will address three fundamental research challenges. First, it will create new machine learning methods for explainable decision making. Second, it will develop new models to generate free-text natural language explanations. Third, it will identify key factors to evaluate the quality of explanations. In the process, the project will also develop aggregated explainability measures and release evaluation benchmarks to support reproducible explainable recommendation research. The project will result in the dissemination of shared data and benchmarks to the Information Retrieval, Data Mining, Recommender System, and broader AI communities.