Showing papers by "Mohammad Aliannejadi published in 2017"

Personalized Keyword Boosting for Venue Suggestion Based on Multiple LBSNs

Abstract: Personalized venue suggestion plays a crucial role in satisfying the users needs on location-based social networks (LBSNs). In this study, we present a probabilistic generative model to map user tags to venue taste keywords. We study four approaches to address the data sparsity problem with the aid of such mapping: one model to boost venue taste keywords and three alternative models to predict user tags. Furthermore, we calculate different scores from multiple LBSNs and show how to incorporate new information from the mapping into a venue suggestion approach. The computed scores are then integrated adopting learning to rank techniques. The experimental results on two TREC collections demonstrate that our approach beats state-of-the-art strategies.

Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion

Abstract: Personalized context-aware venue suggestion plays a critical role in satisfying the users' needs on location-based social networks (LBSNs). In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. The scores are based on user's history of preferences in other cities as well as user's context. We address the data sparsity problem in venue recommendation with the aid of a proposed approach to predict contextually appropriate places. Furthermore, we show how to incorporate different scores to improve the performance of recommendation. The experimental results of our participation in the TREC 2016 Contextual Suggestion track show that our approach beats state-of-the-art strategies.

A Cross-Platform Collection for Contextual Suggestion

Abstract: Suggesting personalized venues helps users to find interesting places on location-based social networks (LBSNs). Although there are many LBSNs online, none of them is known to have thorough information about all venues. The Contextual Suggestion track at TREC aimed at providing a collection consisting of places as well as user context to enable researchers to examine and compare different approaches, under the same evaluation setting. However, the officially released collection of the track did not meet many participants' needs related to venue content, online reviews, and user context. That is why almost all successful systems chose to crawl information from different LBSNs. For example, one of the best proposed systems in the TREC 2016 Contextual Suggestion track crawled data from multiple LBSNs and enriched it with venue-context appropriateness ratings, collected using a crowdsourcing platform. Such collection enabled the system to better predict a venue's appropriateness to a given user's context. In this paper, we release both collections that were used by the system above. We believe that these datasets give other researchers the opportunity to compare their approaches with the top systems in the track. Also, it provides the opportunity to explore different methods to predicting contextually appropriate venues.

Personalized ranking for context-aware venue suggestion

Abstract: Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation These suggestions are often based on matching venues' features with users' preferences, which can be collected from previously visited locations In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, proved that our methodology outperforms state-of-the-art approaches by a significant margin

Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data

Abstract: We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semi-supervised CRF by defining new feature set and altering the label propagation algorithm. Our results demonstrate that our proposed approach significantly improves the performance of the supervised model by utilizing the knowledge gained from the graph.

Personalized Ranking for Context-Aware Venue Suggestion

Abstract: Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.