Recommender systems relieve users from cognitive overloading by predicting preferred items for users. Due to the complexity of interactions between and items, graph neural networks (GNN) use structures effectively model user-item interactions. However, existing GNN approaches have following limitations: 1) user reviews are not adequately modeled in graphs. Therefore, preferences item properties...

There are many algorithms for optimizing the search engine results, ranking takes place according to one or more parameters such as; Backward Links, Forward Links, Content, click through rate and etc. The quality and performance of these algorithms depend on the listed parameters. The ranking is one of the most important components of the search engine that represents the degree of the vitality...

purpose: this study aimed to identify and introduce the most important evaluation and production criteria of audio books based on users’ point of view.method: in terms of methodology, this study was among the quantitative studies and done with descriptive survey. the research instrument was researcher-made questionnaire. population was all users of audiolib website that used from audio books mo...

In a conversational system, determining the user’s focus of attention is crucial to the success of the system. Motivated by previous psycholinguistic findings, we are currently examining how eye gaze contributes to automated identification of user attention during conversation. In particular, we are developing techniques that can predict an objects’ activation in a given time frame based on use...

We describe a technique to determine presence and attention levels of computer users. This technique relies on sonar using hardware that already exists on commodity laptop computers and other electronic devices. It leverages the fact that human bodies have a different effect on sound waves than air and other objects. We conducted a user study in which 20 volunteers used a computer equipped with...