Exploring latent browsing graph for question answering recommendation

Chiang, Meng Fen; Peng, Wen Chih; Yu, Philip S.

doi:10.1007/s11280-011-0146-0

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…Instead shed light solely on users, recent works are more complex and multitudinous. Chiang et al (2012) propose to recommend by a graph-based model. They rely on the user browsing logs and claim language dependence can cause problems in graph based recommender models in Q&A systems and make user generated contents not reliable.…”

Section: Related Workmentioning

confidence: 99%

“…Although it is arguable that some existing works, Guo et al (2008); Dong et al (2015), are based on those tags, we propose to exact the latent domains to avoid the prospective inadequacies. More often, existing works like Chiang et al (2012); Zhang et al (2007); Hanrahan et al (2012); Riahi et al (2012) build profiles for each user, and in our case, this is not feasible due to our monumental data scale.…”

Section: Introductionmentioning

confidence: 99%

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Software expert discovery via knowledge domain embeddings in a collaborative network

Huang¹,

Yao²,

Wang³

et al. 2020

Pattern Recognition Letters

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Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Software expert discovery via knowledge domain embeddings in a collaborative network

Huang¹,

Yao²,

Wang³

et al. 2020

Pattern Recognition Letters

View full text Add to dashboard Cite

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“…Moreover, they show that a browsing graph generated from about 15 days of data is stable enough to be reliable. As a result of its good performance, several variations and improvements of BrowseRank have been proposed in the recent past [33,9,5].…”

Section: Related Workmentioning

confidence: 99%

Image ranking based on user browsing behavior

Trevisiol

Chiarandini

Aiello

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Ranking of images is difficult because many factors determine their importance (e.g., popularity, quality, entertainment value, context, etc.). In social media platforms, ranking also depends on social interactions and on the visibility of the images both inside and outside those platforms. In this context, the application of standard ranking methods is not clearly understood, and neither are the subtleties associated with taking into account social interaction, internal, and external factors. In this paper, we use a large Flickr dataset and investigate these factors by performing an in-depth analysis of several ranking algorithms using both internal (i.e., within Flickr) and external (i.e., links from outside of Flickr) factors. We analyze rankings given by common metrics used in image retrieval (e.g., number of favorites), and compare them with metrics based on page views (e.g., time spent, number of views). In addition, we represent users' navigation by a graph and combine session models with some of these metrics, comparing with PageRank and BrowseRank. Our experiments show significant differences between the rankings, providing insights on the impact of social interactions, internal, and external factors in image ranking.

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“…For example, reachability queries can be used to conduct the ancestor-descendant search in XML databases [4,112], to estimate a given molecule's influence on the expression of genes in biological networks [113], or to determine whether two users are related in social networks [114,115]. Other applications include website analysis [116,117], web usage mining [118,119], and so forth.…”

Section: Introductionmentioning

confidence: 99%

A Study on Time-Dependent Reachability and Route Scheduling in Road Network

Li¹

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For thousands of years, humans have been innovating new technologies to plan their journeys: from looking up the starry sky, to depending on the magnetic compass; from referring to precious ancient maps, to interacting with locals for nearby information. However, these approaches are either inaccurate or hard to grasp by ordinary people. Thanks to the recent rapid development of online map services and GPS devices, we are able to identify where we are on earth, find any place we want to go, and retrieve a route to it. Although it is convenient and fast enough for basic uses, it is still far from optimal. For starters, most systems just provide a shortest path without considering the traffic condition. Secondly, some systems consider the current traffic condition to provide an estimated travel time. However, the lack of estimation for future traffic condition cannot help us plan the travel ahead of time. To make the things worse, the computation that takes traffic condition into consideration grows slower as the planning time interval and the distance grow longer. Therefore, we study how to plan a travel that considers traffic information from the following aspects. The first one is the reachability problem. A road network, or a map, is essentially a graph with nodes representing intersections and edges representing roads. For a well-maintained map, the nodes are reachable to each other. However, this is not always the case when we obtain our map data. For example, the nodes along the boundary might not reach the other nodes on the map. We propose a High-Dimensional Graph Dominance Drawing approach to answer if one node can reach another quickly on large graphs. In fact, it takes only constant time to answer reachability query in road network. We run our algorithm on various of graph structures with different configurations to fully test its performance. The results help us have a deeper understanding on the reachability problem. The second one is the speed profile generation. A speed profile is a set of functions that return the travel time of any road by providing any departure time. Many existing works just assume such a speed profile exists, or generate one synthetically. Other real-life applications tend to use real-time data from sensors monitoring major roads, which is expensive to deploy and unable to cover a large area. In this work, we use historical trajectories of taxis to generate a speed profile. It involves map-matching, speed data collecting, missing value estimation and compression. By using different speed profile for different types of day, we can provide route scheduling that satisfying user's need. Extensive experiments show that our speed profile is accurate and space efficient. The third one is the minimal on-road travel time route scheduling (MORT). This is a general form of all the single criteria path problems. All the existing path finding problem does not allow iii waiting on some vertices along the route, nor can they benefit from it. We extend this problem by allowing waiting. In this...

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Exploring latent browsing graph for question answering recommendation

Cited by 9 publications

References 17 publications

Software expert discovery via knowledge domain embeddings in a collaborative network

Software expert discovery via knowledge domain embeddings in a collaborative network

Image ranking based on user browsing behavior

A Study on Time-Dependent Reachability and Route Scheduling in Road Network

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