7,189 research outputs found
Regularising Factorised Models for Venue Recommendation using Friends and their Comments
Venue recommendation is an important capability of Location-Based Social Networks such as Yelp and Foursquare. Matrix Factorisation (MF) is a collaborative filtering-based approach that can effectively recommend venues that are relevant to the users' preferences, by training upon either implicit or explicit feedbacks (e.g. check-ins or venue ratings) that these users express about venues. However, MF suffers in that users may only have rated very few venues. To alleviate this problem, recent literature have leveraged additional sources of evidence, e.g. using users' social friendships to reduce the complexity of - or regularise - the MF model, or identifying similar venues based on their comments. This paper argues for a combined regularisation model, where the venues suggested for a user are influenced by friends with similar tastes (as defined by their comments). We propose a MF regularisation technique that seamlessly incorporates both social network information and textual comments, by exploiting word embeddings to estimate a semantic similarity of friends based on their explicit textual feedback, to regularise the complexity of the factorised model. Experiments on a large existing dataset demonstrate that our proposed regularisation model is promising, and can enhance the prediction accuracy of several state-of-the-art matrix factorisation-based approaches
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
Venue recommendation aims to assist users by making personalised
suggestions of venues to visit, building upon data available from
location-based social networks (LBSNs) such as Foursquare. A
particular challenge for this task is context-aware venue recommendation
(CAVR), which additionally takes the surrounding context of
the user (e.g. the user’s location and the time of day) into account
in order to provide more relevant venue suggestions. To address the
challenges of CAVR, we describe two approaches that exploit word
embedding techniques to infer the vector-space representations of
venues, users’ existing preferences, and users’ contextual preferences.
Our evaluation upon the test collection of the TREC 2015
Contextual Suggestion track demonstrates that we can significantly
enhance the effectiveness of a state-of-the-art venue recommendation
approach, as well as produce context-aware recommendations
that are at least as effective as the top TREC 2015 systems
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements
Predicting stock market movements is a well-known problem of interest.
Now-a-days social media is perfectly representing the public sentiment and
opinion about current events. Especially, twitter has attracted a lot of
attention from researchers for studying the public sentiments. Stock market
prediction on the basis of public sentiments expressed on twitter has been an
intriguing field of research. Previous studies have concluded that the
aggregate public mood collected from twitter may well be correlated with Dow
Jones Industrial Average Index (DJIA). The thesis of this work is to observe
how well the changes in stock prices of a company, the rises and falls, are
correlated with the public opinions being expressed in tweets about that
company. Understanding author's opinion from a piece of text is the objective
of sentiment analysis. The present paper have employed two different textual
representations, Word2vec and N-gram, for analyzing the public sentiments in
tweets. In this paper, we have applied sentiment analysis and supervised
machine learning principles to the tweets extracted from twitter and analyze
the correlation between stock market movements of a company and sentiments in
tweets. In an elaborate way, positive news and tweets in social media about a
company would definitely encourage people to invest in the stocks of that
company and as a result the stock price of that company would increase. At the
end of the paper, it is shown that a strong correlation exists between the rise
and falls in stock prices with the public sentiments in tweets.Comment: 6 pages 4 figures Conference Pape
Toward Word Embedding for Personalized Information Retrieval
This paper presents preliminary works on using Word Embedding (word2vec) for
query expansion in the context of Personalized Information Retrieval.
Traditionally, word embeddings are learned on a general corpus, like Wikipedia.
In this work we try to personalize the word embeddings learning, by achieving
the learning on the user's profile. The word embeddings are then in the same
context than the user interests. Our proposal is evaluated on the CLEF Social
Book Search 2016 collection. The results obtained show that some efforts should
be made in the way to apply Word Embedding in the context of Personalized
Information Retrieval
- …