Collaborative topic modeling for recommending scientific articles

Wang, Chong; Blei, David M.

doi:10.1145/2020408.2020480

Cited by 1,302 publications

(1,112 citation statements)

References 14 publications

Supporting

Mentioning

1,103

Contrasting

Unclassified

Order By: Relevance

“…Collaborative topic regression (denoted CTR): This is the matrix factorization with topic modeling applied to the content of items described in [26]. For our data sets, items are POIs and content of user reviews on POIs.…”

Section: Rating Accuracy Of Individual Poismentioning

confidence: 99%

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang

Liang

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

show abstract

Section: Rating Accuracy Of Individual Poismentioning

confidence: 99%

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang

Liang

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…To prove the efficiency of our proposed framework, we use CiteULike dataset in [9]. The dataset contains CiteULike users' profile and users' reading information from 2004 to 2010.…”

Section: Datasetmentioning

confidence: 99%

“…2.Afterwards, we compare the result of our work with CTR algorithm proposed in [9], since we adopted their dataset. Also CTR is based on topic model, which makes the comparison just.…”

Section: Experiments Designmentioning

confidence: 99%

See 1 more Smart Citation

Increasing Serendipity of Recommender System with Ranking Topic Model

Xiao¹,

Che²,

Miao³

et al. 2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

There are thousands of academic paper published each year, it is quite hard for researchers who enters a new field to discover relevant paper and novel paper to read, which we characterize as choice overload problem. Recommender system can help to alleviate the problem, but recommender system suffers from the intention gap problem which is the incapability of the system to accurately guess users' intentions. We proposed a ranking topic model based semantic recommendation framework which helps to introduce serendipity to the system. First, the proposed ranking topic model reorders learnt topic distributions according to users' intentions. Then, learnt ordered topics are used as features to rank papers in the library according to the relevancy to user query. At the same time, ranked topics also provide novelty to the results. Since there is little work on how to evaluate the serendipity degree of recommender system, we proposed two measure to evaluate this metric. We performed empirical experiments to test the efficiency of proposed framework with state-of-the-art counterparts, the comparison results revealed the superiority of our proposed algorithms. In the end, we illustrated our algorithms with an example and pointed out future research directions.

show abstract

“…Hence, the performance suffers when the data have low signal-noise ratio using classic topic models. Several works for different applications have considered this problem and allow the model to have different strategies to handle noise [11,21,25,16]. Three of these, [11,21,16], are designed for specific (non-classification) tasks, while the fourth, [25], is heuristic in the sense that it introduces an entropic regularizer.…”

Section: Introductionmentioning

confidence: 99%

How to Supervise Topic Models

Zhang

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Supervised topic models are important machine learning tools which have been widely used in computer vision as well as in other domains. However, there is a gap in the understanding of the supervision impact on the model. In this paper, we present a thorough analysis on the behaviour of supervised topic models using Supervised Latent Dirichlet Allocation (SLDA) and propose two factorized supervised topic models, which factorize the topics into signal and noise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective and factorized topic models are able to enhance the performance.

show abstract

Collaborative topic modeling for recommending scientific articles

Cited by 1,302 publications

References 14 publications

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Increasing Serendipity of Recommender System with Ranking Topic Model

How to Supervise Topic Models

Contact Info

Product

Resources

About