Learning to cluster web search results

Zeng, Hua-Jun; He, Qi-Cai; Chen, Zheng; Ma, Wei‐Ying; Ma, Jinwen

doi:10.1145/1008992.1009030

Cited by 453 publications

(277 citation statements)

References 7 publications

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“…In [19] the last two were refined in 10 more classes. In [27,16] simple attributes were used to predict the need behind the query. Our models also shed light in this problem.…”

Section: Related Workmentioning

confidence: 99%

Modeling User Search Behavior

Baeza-Yates

Hurtado

Mendoza

et al.

Third Latin American Web Congress (LA-WEB'2005)

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“…In [19] the last two were refined in 10 more classes. In [27,16] simple attributes were used to predict the need behind the query. Our models also shed light in this problem.…”

Section: Related Workmentioning

confidence: 99%

Modeling User Search Behavior

Baeza-Yates

Hurtado

Mendoza

et al.

Third Latin American Web Congress (LA-WEB'2005)

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“…There has been much work in recent years clustering tagged videos. For example, Zeng et al [5] utilize a learned model over text-based tags to cluster search results. Schroff et al [6] cluster videos by location based tags.…”

Section: Clustermentioning

confidence: 99%

Discovering Video Clusters from Visual Features and Noisy Tags

Vahdat

Zhou

Mori

2014

Computer Vision – ECCV 2014

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Abstract. We present an algorithm for automatically clustering tagged videos. Collections of tagged videos are commonplace, however, it is not trivial to discover video clusters therein. Direct methods that operate on visual features ignore the regularly available, valuable source of tag information. Solely clustering videos on these tags is error-prone since the tags are typically noisy. To address these problems, we develop a structured model that considers the interaction between visual features, video tags and video clusters. We model tags from visual features, and correct noisy tags by checking visual appearance consistency. In the end, videos are clustered from the refined tags as well as the visual features. We learn the clustering through a max-margin framework, and demonstrate empirically that this algorithm can produce more accurate clustering results than baseline methods based on tags or visual features, or both. Further, qualitative results verify that the clustering results can discover sub-categories and more specific instances of a given video category.

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“…One of the wonderful benefits of rating is that how the customers feel about brand, what they like and dislike about products and how can improve the overall product, which help products rank higher. H.-J Zeng [3] and J.-R Wen [6] illustrated the importance of clustering. Feedbacks for each part of product can be collected and can be effectively clustered by using k-means clustering algorithm which is effective and simple.…”

Section: Feedbacks and Ratingmentioning

confidence: 99%

Inferring User Goals Using Customer Feedback andAnalyzing Customer Behavior

Gayathri¹,

Nandhakumar²,

Gokulavani³

et al. 2013

IJCATR

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Many enterprises devote a significant portion of their budget to new product development (NPD) and marketing to make their products distinctive from those of competitors, and to know better the needs and expectations of consumers. Hence, knowledge and suggestions on customer demand and consumption experience has become an important information and asset for enterprises. Inferring user search goals are very important in improving the efficiency. For this, feedbacks are obtained from the customer. The submitted feedbacks are clustered as feedback sessions. Pseudo-documents are generated to better understand the clustered feedbacks. K-means clustering algorithm is used to cluster the feedbacks. These feedbacks are very useful in development of new product. Ranking model is used to provide ranks to the products based on the customer feedbacks. Hence knowledge and feedback from customers has become important information. Product design is integrated with the knowledge of customers. Users may also pose their questions about the products which are added when it is suitable. Hence customer behaviour can be analysed from their posed questions and response. Finally, evaluation criterion is described to evaluate the performance of new product.

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Learning to cluster web search results

Cited by 453 publications

References 7 publications

Modeling User Search Behavior

Modeling User Search Behavior

Discovering Video Clusters from Visual Features and Noisy Tags

Inferring User Goals Using Customer Feedback andAnalyzing Customer Behavior

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