3,268 research outputs found
Overview of the personalized and collaborative information retrieval (PIR) track at FIRE-2011
The Personalized and collaborative Information Retrieval (PIR) track at FIRE 2011 was organized with an aim to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by collecting additional meta-information during the topic (query) development process. A controlled query generation process through task-based activities with activity logging was used for each topic developer to construct the final list of topics. The standard ad-hoc collection is thus accompanied by a new set of thematically related topics and the associated log information. We believe this can better simulate a real-world search scenario and encourage mining user information from the logs to improve IR effectiveness. A set of 25 TREC formatted topics and the associated metadata of activity logs were released for the participants to use. In this paper we illustrate the data construction phase in detail and also outline two simple ways of using the additional information from the logs to improve retrieval effectiveness
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match usersā personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
We address personalization issues of image captioning, which have not been
discussed yet in previous research. For a query image, we aim to generate a
descriptive sentence, accounting for prior knowledge such as the user's active
vocabularies in previous documents. As applications of personalized image
captioning, we tackle two post automation tasks: hashtag prediction and post
generation, on our newly collected Instagram dataset, consisting of 1.1M posts
from 6.3K users. We propose a novel captioning model named Context Sequence
Memory Network (CSMN). Its unique updates over previous memory network models
include (i) exploiting memory as a repository for multiple types of context
information, (ii) appending previously generated words into memory to capture
long-term information without suffering from the vanishing gradient problem,
and (iii) adopting CNN memory structure to jointly represent nearby ordered
memory slots for better context understanding. With quantitative evaluation and
user studies via Amazon Mechanical Turk, we show the effectiveness of the three
novel features of CSMN and its performance enhancement for personalized image
captioning over state-of-the-art captioning models.Comment: Accepted paper at CVPR 201
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
SE-PQA: Personalized Community Question Answering
Personalization in Information Retrieval is a topic studied for a long time.
Nevertheless, there is still a lack of high-quality, real-world datasets to
conduct large-scale experiments and evaluate models for personalized search.
This paper contributes to filling this gap by introducing SE-PQA (StackExchange
- Personalized Question Answering), a new curated resource to design and
evaluate personalized models related to the task of community Question
Answering (cQA). The contributed dataset includes more than 1 million queries
and 2 million answers, annotated with a rich set of features modeling the
social interactions among the users of a popular cQA platform. We describe the
characteristics of SE-PQA and detail the features associated with questions and
answers. We also provide reproducible baseline methods for the cQA task based
on the resource, including deep learning models and personalization approaches.
The results of the preliminary experiments conducted show the appropriateness
of SE-PQA to train effective cQA models; they also show that personalization
remarkably improves the effectiveness of all the methods tested. Furthermore,
we show the benefits in terms of robustness and generalization of combining
data from multiple communities for personalization purposes
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of
predefined ones is coined as document tagging in this work. Accurate tagging of
articles can benefit several downstream applications such as recommendation and
search. In this work, we propose a novel yet simple approach called DocTag2Vec
to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two
popular models for learning distributed representation of words and documents.
In DocTag2Vec, we simultaneously learn the representation of words, documents,
and tags in a joint vector space during training, and employ the simple
-nearest neighbor search to predict tags for unseen documents. In contrast
to previous multi-label learning methods, DocTag2Vec directly deals with raw
text instead of provided feature vector, and in addition, enjoys advantages
like the learning of tag representation, and the ability of handling newly
created tags. To demonstrate the effectiveness of our approach, we conduct
experiments on several datasets and show promising results against
state-of-the-art methods.Comment: 10 page
- ā¦