Survey on the Use of Feedback Sessions for Inferring User Search Goals

Largest source of web traffic are search engines. Search engines are being used by different kind of users for different purpose. When users are searching something they have a different search goal in mind. Thus the queries are mostly ambiguous one. In order to improve search engine relevance and thus user experience inference and analysis of user search is required. To get the best results it is needful to capture different user search goals. This paper first talks about the different ways of inferring user search goals. Then insights of new approach has been discussed. A new algorithm firstly specifies a framework to analyze user search goals by clustering feedback sessions. There should be a proper way to represent these feedback sessions. In the second step of this algorithm pseudo-documents are prepared to represent feedback sessions. With this original results are restructured. This in turn is used to select optimal user search goals

A New Approach of Clustering Feedback Sessions for Inferring User Search Goals

Internet information is growing every day exponentially. In order to find out the exact required information from this web search engines has become absolutely necessary tool for the web users. It has also become more difficult to provide user the required information. When Different users provide an ambiguous query to a search engine, they might be having different search goals. Therefore, it is required to find and analyze user search goals to improve the performance of a search engine and user experience. By representing the results in cluster we find out different user search goals for a query. It has advantages in improving search engine relevance and user experience. It extends the delivery and quality of internet information services to the end user. It also improves performance of Web server system. Query classification, search result reorganization and session boundary detection are the approaches attempt to find out user search goals. But the mentioned approaches has many limitations. A new approach has been implemented that overcomes the limitations and analyze, discover user search goals using feedback sessions. This approach first takes the user search query. For each single result of the search query pseudo-documents are generated. Using K-means++ clustering algorithm, these pseudo-documents are clustered. Each cluster can be considered as one user search goal. Finally in restructured result is given to the user where each URL is categorized into a cluster centered by the inferred search goals. Then depending upon user click through, results are restructured and represented to the user in order to satisfy the information need. DOI: 10.17762/ijritcc2321-8169.15071

Automatic Annotating Search Results with Relevance Feedback for User Search Goals

Information retrieved form web database which contain data in html format. For more understanding of user need to extract the html pages and assign labels mean Data Alignment is need for Data units for html documents . Then, for each group annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. Users search with accuracy and speed goals is to study law. This method limits the conditions suffered in the search accuracy and speed. Currently the main aim for more improvements and approaches to Web user satisfaction of search is the basis for the goals. Users search for goals different methods literature review to present the new framework and proposed methods and insightful analysis algorithms and evaluate its performance. First, we propose framework automatic annotation for retrieved documents by clustering the same contain documents and assign data units for each cluster . Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Finally, we propose a new criterion “Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods. DOI: 10.17762/ijritcc2321-8169.15076

Reconstructing of search Results in web Search Engine Using Feedback Activities

No Abstrac

Enhanced Re-ranking and Semantic Similarity Algorithm for Image Search Goals using Click-through Logs

The objective of the proposal is to analyze the user search goals for a query which can be very useful in improving search engine relevance and user experience. Although the research on inferring user goals or intents for text search has received much attention, little has been proposed for image search with visual information. In this project, we propose a novel approach to capture user search goals in image search by exploring images which are extracted by mining single sessions in user click-through logs to reflect user information needs. Moreover, we also propose a novel evaluation criterion to determine the number of user search goals for a query. Modified re-ranking and semantic similarity algorithm are part of this proposal. Experimental results demonstrate the effectiveness of the proposed method

Implicit Measures of Lostness and Success in Web Navigation

In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The user’s task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13