Enhanced web log based recommendation by personalized retrieval

University of Technology, Sydney. Faculty of Engineering and Information Technology.With the rapid development of the Internet and WWW, it is more and more important for people to access quality web information. Thus the problem of enabling users to quickly and accurately find information has become an urgent issue. As one of the basic ways to solve this problem, personalized information services have been focusing on fulfilling the personalized information requirements of different users based on their actual demands, preference characteristics, behaviour patterns, etc. This thesis focuses on enhancing web log based recommendation by personalized retrieval, and its main works and innovations include: • For personalized retrieval, the thesis proposes two models to improve user experience and optimize search performance. The first is a query suggestion model based on query semantics and click-through data. This model calculates the subject relevance between queries, and then combines the semantic information and the relevance of the query-click matrix model as this can effectively eliminate the ambiguity and input errors of reminder queries. The second is a collaborative filtering retrieval model based on local and global features. By the integration of the local and global characteristics of the accessed information, this model overcomes the limitations of a single feature, and increases the degree of application of the retrieval model. • For recommendation by personalized retrieval, we propose two recommendation models based on the web log. The first is based on the user’s atomic retrieval transaction sequence and the browse characteristics. This model decomposes search transactions, and calculates the user’s degree of interest on the search term, which allows users to query information more clearly. Further, it incorporates the user feedback on the search results evaluation value, which overcomes the shortcomings of the model based on content filtering. The second model is based on user interests association findings, which can be used to: find the relationship between resources accessed by users, extract the associations of user interests, and address the problem of user interests isolation

Query Click and Text Similarity Graph for Query Suggestions

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion

Network Capacity Bound for Personalized PageRank in Multimodal Networks

In a former paper the concept of Bipartite PageRank was introduced and a theorem on the limit of authority flowing between nodes for personalized PageRank has been generalized. In this paper we want to extend those results to multimodal networks. In particular we introduce a hypergraph type that may be used for describing multimodal network where a hyperlink connects nodes from each of the modalities. We introduce a generalisation of PageRank for such graphs and define the respective random walk model that can be used for computations. we finally state and prove theorems on the limit of outflow of authority for cases where individual modalities have identical and distinct damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373

Enhanced Web Search Engines with Query-Concept Bipartite Graphs

With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications

Multiple Models for Recommending Temporal Aspects of Entities

Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.Comment: In proceedings of the 15th Extended Semantic Web Conference (ESWC 2018

Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol

FLEET: Butterfly Estimation from a Bipartite Graph Stream

We consider space-efficient single-pass estimation of the number of butterflies, a fundamental bipartite graph motif, from a massive bipartite graph stream where each edge represents a connection between entities in two different partitions. We present a space lower bound for any streaming algorithm that can estimate the number of butterflies accurately, as well as FLEET, a suite of algorithms for accurately estimating the number of butterflies in the graph stream. Estimates returned by the algorithms come with provable guarantees on the approximation error, and experiments show good tradeoffs between the space used and the accuracy of approximation. We also present space-efficient algorithms for estimating the number of butterflies within a sliding window of the most recent elements in the stream. While there is a significant body of work on counting subgraphs such as triangles in a unipartite graph stream, our work seems to be one of the few to tackle the case of bipartite graph streams.Comment: This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Seyed-Vahid Sanei-Mehri, Yu Zhang, Ahmet Erdem Sariyuce and Srikanta Tirthapura. "FLEET: Butterfly Estimation from a Bipartite Graph Stream". The 28th ACM International Conference on Information and Knowledge Managemen

查询推荐研究综述

查询推荐是一种提高用户搜索效率的重要技术，其核心任务是帮助用户构造有效查询并以此准确描述用户信息需求。作为当今搜索引擎的核心技术之一，查询推荐吸引了学术界和工业界的广泛关注，一直以来都是信息检索领域中重要的研究主题。本文以国内外会议、期刊发表的有关查询推荐研究的文献为对象，利用归纳总结方法，首先详细梳理了查询推荐中主流方法&mdash;&mdash;基于简单共现信息的方法、基于图模型的方法以及融合多种信息的方法，然后总结评述了评测方法与指标，最后分析了未来可能的研究方向。</p

Social Search: retrieving information in Online Social Platforms -- A Survey

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further