11110240
Using Temporal Information in Collaborative Filtering: An Empirical Study
Collaborative filtering is a widely used and proven method of building recommender systems that provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an important factor in some e-commerce environments (such as a mobile environment as a result of limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy. In this paper, we present a variety of temporal information and investigate how such temporal information affects the accuracy of collaborative filtering-based recommender systems. The temporal information includes item launch time, user buying time, the time difference between the two, as well as several combinations of these three types of temporal information. We conducted several experiments on a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results show that the effectiveness of temporal information depends on the type of items and the user group of a given e-commerce environment. Our findings give insights on how to incorporate temporal information to maximize the efficiency of collaborative filtering in various e-commerce environments.
3876714
A Time-Based Recommender System Using Implicit Feedback
Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations based on explicit ratings on items from other users. However, in some ecommerce environments such as a mobile environment, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy. Based on this method, we built a recommender system for a mobile e-commerce environment and conducted several experiments. Empirical results show our collaborative filtering-based recommender system to be significantly more accurate than a pure collaborative filtering system. Our approach can be applied to building collaborative filtering-based recommender systems for any ecommerce environment without explicit feedback.
11709593
Transition: Recommending Mobile Content using Content Diffusion and User Preference
An improved release coating for heat transferable laminate and improved method for curing the release coating for heat transferable laminates wherein an ink design layer is transferred from a carrier web onto an article such as a plastic or glass container upon application of heat and pressure. The improved release coating does not contain wax components but exhibits release properties similar to wax based formulations. The improved release contains U-V curable components which are cured by exposure to ultraviolet light after the laminate containing the ink design, is transferred to a receiving article. The heat transferable laminate preferably includes a barrier layer intermediate the release layer and ink design layer. The barrier layer also contains U-V curable components. The barrier layer is cured by exposure to ultraviolet radiation during formation of the laminate structure and prior to transfer of the laminate to a receiving article. The improved release formulation upon curing by ultraviolet radiation produces a hard, glossy, optically clear protective layer exhibiting enhanced resistance to abrasion, scuffing and solvent attack.
38854847
A time-based approach to effective recommender systems using implicit feedback
Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user's purchase time and the item's launch time in order to increase recommendation accuracy. Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.
15260516
A Similarity Measure for Collaborative Filtering with Implicit Feedback
Collaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well for explicit ratings, but do not capture real similarity from the ratings derived from implicit feedback. This paper identifies some problems that existing similarity measures have with implicit ratings by analyzing the characteristics of implicit feedback, and proposes a new similarity measure called Inner Product that is more appropriate for implicit ratings. We conducted experiments on user-based collaborative filtering using the proposed similarity measure for two e-commerce environments. Empirical results show that our similarity measure better captures similarities for implicit ratings and leads to more accurate recommendations. Our inner product-based similarity measure could be useful for CF-based recommender systems using implicit ratings in which negative ratings are difficult to be incorporated.
31650942
An empirical study on effectiveness of temporal information as implicit ratings
Collaborative filtering is a widely used and proven method of building recommender systems, which provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an especially important factor in some e-commerce environments (such as a mobile environment, due to limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy. This paper presents a variety of temporal information including item launch time, user buying time, the time difference between the two, as well as several combinations of these three. We conducted an empirical study on how temporal information affects the accuracy of a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results show the degree of effectiveness of a variety of temporal information. The empirical results give insight on how to incorporate temporal information to maximize the effectiveness of collaborative filtering in various e-commerce environments.
61509689
Blackboard scheduler control knowledge for heuristic classification: Representation and inference
16981497
Organizing and retrieving class components based on types for reuse
Reusing existing software components improves software development productivity. Classes are basic building blocks in object-oriented programming. One of the central issues in reusing existing classes in object-orient programming is to find suitable reusable classes. This paper describes a method of organizing the OO class library for retrieving classes using type information. The method is based on (i) grouping classes based on the number of methods and the number of arguments of methods in classes and (ii) linking both groups and classes based on potential reusability relationships. This structured reuse library allows the class retrieval process to be done efficiently by narrowing the search space using groups and links. The user can also navigate the reuse library and learn about potential reusability among class components.
16378618
e-Gov Net Based Emergency Management Service
Over the past decade years, there has been a significant evolution in thinking about not only emergency management but also disasters among IT-related workers, policymaker, and other involved in the disaster fields. In the view point of ubiquitous computing environment, the current e-Gov net has some problems in business process and in service due to the unbalanced management of administration. Therefore, there are limitations for improving the service to the people. It is also need to e-Gov net acceleration propulsion correspond to the change of technology such as BcN, IPv6, USN as well as the change of social environment. What is more, it is needed to systematic research corresponding to the needs of future-oriented government service which is available to e-Gov net guaranteeing in security and emergency management as well. In this paper, the methodology for developing new services is proposed, which is based on e-Gov net. Secondly, the basics of the BC (business continuity) and the DR (disaster recovery) of IT-centric perspective are introduced.
12684408
Intelligent and Effective Digital Watermarking Scheme for Mobile Content Service
In this paper, digital watermarking scheme that is intelligent and effective in protecting a copyright of image data under image transformation such as scaling, rotation, and JPEG lossy compression is proposed. Especially, the proposed scheme is a robust algorithm for mobile content service. It means that the proposed watermarking is robustness to protect impulse noise attacks. The watermark and inserted watermark are very different from each other. The original watermark is recomposed using the code granted to a copyrighter or customer. The proposed algorithm is an extension of spread-spectrum watermarking algorithm that obtains the convolution value of PN sequence. Correlator and decision circuit in our algorithm are required. Because just a little watermark is embedded in the original image, this decision circuit plays an important role for detecting this watermark and then the intelligent method for the decision must be required. Degradation of image quality between original image and watermarked image in order to confirm required invisibility in the digital watermarking system and watermark robustness to protect various attacks from the outside are analyzed using the value of PSNR and recovered rate of watermark image. The experimental result shows that image quality is less degraded as the PSNR of 93.75 dB. It is also observed that excellent watermark detection is achieved under an image transformation and JPEG lossy compression, and watermark can be effectively detected in case of the image inserted impulse noise, which PSNR is 7.97 dB.
15172968
A structured approach to retrieving functions by types
A solid state area imaging apparatus having a photosensor matrix array disposed at a front surface area of a substrate and having a charge transfer arrangement for transferring charges generated in the photosensors of the matrix so as to convert them into a serial signal. It also includes a penetrative charge transfer arrangement for transferring the charge from the front surface area of the substrate to the back surface area of the substrate, and which has a static induction transistor structure. The charges are transferred vertically through a channel formed in the structure of the static induction transistor. This penetrative charge transfer arrangement may be used as a part of the transfer path for generating the serial signal or a path for removing excess charges which are generated in the photosensors.