Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

A fast tabu search algorithm for the group shop scheduling problem

Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.

In search of the Korean digital wave

This article sets the context for this special themed issue on the 'Korean digital wave' by considering the symbiotic relationship between digital technologies, their techniques and practices, their uses and the affordances they provide, and Korea's 'compressed modernity' and swift industrialisation. It underscores the importance of interrogating a range of groundbreaking developments and innovations within Korea's digital mediascapes, and its creative and cultural industries, in order to gain a complex understanding of one of Australia's most significant export markets and trading partners. Given the financial and political commitment in Australia to a high-speed broadband network that aims to stimulate economic and cultural activity, recent technological developments in Korea, and the double-edged role played by government policy in shaping the 'Korean digital wave', merit close attention from media and communications scholars.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

In search of a youth justice pedagogy? A commentary

Purpose – This paper seeks to look at youth justice (YJ) personnel training and education and the recommendations about it made in Time for a Fresh Start. Design/methodology/approach – The pedagogic tensions that currently shape YJ training are described – particularly those around the question of instructionalism vs education and what “specialist” means in the context of YJ. Findings – The paper suggests that the authors of Time for a Fresh Start missed the opportunity to better serve the public and young people's interests by neither acknowledging the pedagogic tensions nor articulating what a “specialist” “YJ” professional training can mean in twenty-first century England and Wales. Originality/value – The paper highlights an urgent need for an open debate between academics, practitioners and policy makers about YJ pedagogy.

Can you describe him for me? A technique for semantic person search in video

From a law enforcement standpoint, the ability to search for a person matching a semantic description (i.e. 1.8m tall, red shirt, jeans) is highly desirable. While a significant research effort has focused on person re-detection (the task of identifying a previously observed individual in surveillance video), these techniques require descriptors to be built from existing image or video observations. As such, person re-detection techniques are not suited to situations where footage of the person of interest is not readily available, such as a witness reporting a recent crime. In this paper, we present a novel framework that is able to search for a person based on a semantic description. The proposed approach uses size and colour cues, and does not require a person detection routine to locate people in the scene, improving utility in crowded conditions. The proposed approach is demonstrated with a new database that will be made available to the research community, and we show that the proposed technique is able to correctly localise a person in a video based on a simple semantic description.

Orthogonality defect and reduced search-space size for solving integer least-squares problems

In the context of ambiguity resolution (AR) of Global Navigation Satellite Systems (GNSS), decorrelation among entries of an ambiguity vector, integer ambiguity search and ambiguity validations are three standard procedures for solving integer least-squares problems. This paper contributes to AR issues from three aspects. Firstly, the orthogonality defect is introduced as a new measure of the performance of ambiguity decorrelation methods, and compared with the decorrelation number and with the condition number which are currently used as the judging criterion to measure the correlation of ambiguity variance-covariance matrix. Numerically, the orthogonality defect demonstrates slightly better performance as a measure of the correlation between decorrelation impact and computational efficiency than the condition number measure. Secondly, the paper examines the relationship of the decorrelation number, the condition number, the orthogonality defect and the size of the ambiguity search space with the ambiguity search candidates and search nodes. The size of the ambiguity search space can be properly estimated if the ambiguity matrix is decorrelated well, which is shown to be a significant parameter in the ambiguity search progress. Thirdly, a new ambiguity resolution scheme is proposed to improve ambiguity search efficiency through the control of the size of the ambiguity search space. The new AR scheme combines the LAMBDA search and validation procedures together, which results in a much smaller size of the search space and higher computational efficiency while retaining the same AR validation outcomes. In fact, the new scheme can deal with the case there are only one candidate, while the existing search methods require at least two candidates. If there are more than one candidate, the new scheme turns to the usual ratio-test procedure. Experimental results indicate that this combined method can indeed improve ambiguity search efficiency for both the single constellation and dual constellations respectively, showing the potential for processing high dimension integer parameters in multi-GNSS environment.

Relationship between the nature of the search task types and query reformulation behaviour

Success of query reformulation and relevant information retrieval depends on many factors, such as users’ prior knowledge, age, gender, and cognitive styles. One of the important factors that affect a user’s query reformulation behaviour is that of the nature of the search tasks. Limited studies have examined the impact of the search task types on query reformulation behaviour while performing Web searches. This paper examines how the nature of the search tasks affects users’ query reformulation behaviour during information searching. The paper reports empirical results from a user study in which 50 participants performed a set of three Web search tasks – exploratory, factorial and abstract. Users’ interactions with search engines were logged by using a monitoring program. 872 unique search queries were classified into five query types – New, Add, Remove, Replace and Repeat. Users submitted fewer queries for the factual task, which accounted for 26%. They completed a higher number of queries (40% of the total queries) while carrying out the exploratory task. A one-way MANOVA test indicated a significant effect of search task types on users’ query reformulation behaviour. In particular, the search task types influenced the manner in which users reformulated the New and Repeat queries.

The search for individualism : Self-expression through product personalisation

This research paper explores the impact product personalisation has upon product attachment and aims to develop a deeper understanding of why, how and if consumers choose to do so. The current research in this field is mainly based on attachment theories and is predominantly product specific. This paper researches the link between product attachment and personalisation through in-depth, semi-structured interviews, where the data has been thematically analysed and broken down into three themes, and nine sub-themes. It was found that participants did become more attached to products once they were personalised and the reasons why this occurred varied. The most common reasons that led to personalisation were functionality and usability, the expression of personality through a product and the complexity of personalisation. The reasons why participants felt connected to their products included strong emotions/memories, the amount of time and effort invested into the personalisation, a sense of achievement. Reasons behind the desire for personalisation included co-designing, expression of uniqueness/individualism and having choice for personalisation. Through theme and inter-theme relationships, many correlations were formed, which created the basis for design recommendations. These recommendations demonstrate how a designer could implement the emotions and reasoning for personalisation into the design process.

Finding additional semantic entity information for search engines

Entity-oriented search has become an essential component of modern search engines. It focuses on retrieving a list of entities or information about the specific entities instead of documents. In this paper, we study the problem of finding entity related information, referred to as attribute-value pairs, that play a significant role in searching target entities. We propose a novel decomposition framework combining reduced relations and the discriminative model, Conditional Random Field (CRF), for automatically finding entity-related attribute-value pairs from free text documents. This decomposition framework allows us to locate potential text fragments and identify the hidden semantics, in the form of attribute-value pairs for user queries. Empirical analysis shows that the decomposition framework outperforms pattern-based approaches due to its capability of effective integration of syntactic and semantic features.