Deep Sequential Models for Task Satisfaction Prediction

Detecting and understanding implicit signals of user satisfaction are essential for experimentation aimed at predicting searcher satisfaction. As retrieval systems have advanced, search tasks have steadily emerged as accurate units not only to capture searcher's goals but also in understanding how well a system is able to help the user achieve that goal. However, a major portion of existing work on modeling searcher satisfaction has focused on query level satisfaction. The few existing approaches for task satisfaction prediction have narrowly focused on simple tasks aimed at solving atomic information needs. In this work we go beyond such atomic tasks and consider the problem of predicting user's satisfaction when engaged in complex search tasks composed of many different queries and subtasks. We begin by considering holistic view of user interactions with the search engine result page (SERP) and extract detailed interaction sequences of their activity. We then look at query level abstraction and propose a novel deep sequential architecture which leverages the extracted interaction sequences to predict query level satisfaction. Further, we enrich this model with auxiliary features which have been traditionally used for satisfaction prediction and propose a unified multi-view model which combines the benefit of user interaction sequences with auxiliary features. Finally, we go beyond query level abstraction and consider query sequences issued by the user in order to complete a complex task, to make task level satisfaction predictions. We propose a number of functional composition techniques which take into account query level satisfaction estimates along with the query sequence to predict task level satisfaction. Through rigorous experiments, we demonstrate that the proposed deep sequential models significantly outperform established baselines at both query and task satisfaction prediction. Our findings have implications on metric development for gauging user satisfaction and on designing systems which help users accomplish complex search tasks

In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

Peer reviewedPostprin

AIDA: ab initio domain assembly server.

AIDA: ab initio domain assembly server, available at http://ffas.burnham.org/AIDA/ is a tool that can identify domains in multi-domain proteins and then predict their 3D structures and relative spatial arrangements. The server is free and open to all users, and there is an option for a user to provide an e-mail to get the link to result page. Domains are evolutionary conserved and often functionally independent units in proteins. Most proteins, especially eukaryotic ones, consist of multiple domains while at the same time, most experimentally determined protein structures contain only one or two domains. As a result, often structures of individual domains in multi-domain proteins can be accurately predicted, but the mutual arrangement of different domains remains unknown. To address this issue we have developed AIDA program, which combines steps of identifying individual domains, predicting (separately) their structures and assembling them into multiple domain complexes using an ab initio folding potential to describe domain-domain interactions. AIDA server not only supports the assembly of a large number of continuous domains, but also allows the assembly of domains inserted into other domains. Users can also provide distance restraints to guide the AIDA energy minimization

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field