Using content-based filtering in a system of recommendation in the context of digital mobile interactive tv
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Using content-based filtering in a system of recommendation in the context of digital mobile interactive tv

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Using content-based filtering in a system of recommendation in the context of digital mobile interactive tv Using content-based filtering in a system of recommendation in the context of digital mobile interactive tv Document Transcript

  • Using Content-Based Filtering in a System of Recommendation in the Context of Digital Mobile Interactive TV Elaine Cecília Gatto, Sergio Donizetti Zorzo IEEE Conference Publishing Computer Science Department. Federal University of São Carlos – UFSCar Highway Washington Luís, Km 235, PO Box 676, 13565-9. São Carlos, São Paulo, Brazil. {elaine_gatto, zorzo}@dc.ufscar.br. Abstract-Recommendation systems provide suggestions based on information about the preferences of users. The filtering information is used by recommender systems for the processing of information and suggestions to users and content-based filtering is an approach to filtering information widely used in recommender systems. Content-Based Filtering on analyzing the correlation of the content of items with the profile, suggesting relevant items and discarding the irrelevant. Widely used on the Internet, recommendation systems are being studied for use in the context of Digital TV, there are already several studies in this direction. Just as occurs on the Internet, recommendation systems can be used in Digital TV for recommendation of TV programs, advertising and publicity and also electronic commerce. Thus, the items in the context of digital TV, may be programs, publicity / advertising and the products to be sold. Applying Content Filtering Based on the recommendation of programs, for example, it should correlate the content of these programs with user preferences, which in this scenario are the types of programs he has preferred to watch. This paper presents the studies performed with Content Filtering Based on Data Applied to Digital TV. The studies seek to observe and evaluate how some techniques of content-based filtering can be used in recommendation systems in the context of Digital TV. I. INTRODUCTION Digital TV implementation in Brazil provides new markets which can be explored. Well-succeeded technologies as those in Web environment, for example, can be applied in Digital TV domain and achieve the same success. The interaction either through the remote control or the cell phone keyboard etc by the user today, will allow many applications to be carried to this environment. One of the areas which has been extensively studied and is well-succeeded in the Web is that of personalization. There are some surveys concerning recommendation systems for Digital TV as for example [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] among others. Recommendation systems can contribute to a better use of Digital TV in residences, in groups or individually, in a cell phone, for example. These systems can help the user to choose the program, avoiding waste of time and of course, suggesting to the user programs which really interest him. Moreover, recommendation systems can be applied to publicity and advertisement on Digital TV, as well as in the T-Commerce. This work is organized as follows: section 1 introduces the paper, section 2 presents a comparison between digital TV and portable devices for homes, section 3 presents related works, section 4 talks about content-based filtering, section 5 presents our recommendation system, Section 6 talks about the characteristics of households, the EPG, the user history and methodology used for the tests, Section 7 talks about the results and Section 8 presents the conclusion. II. COMPARING IDTV IN RESIDENCES AND IDTV FOR CELL PHONES The use of IDTV for cell phones will quickly boom due to the increasingly quantity of these devices surpassing television sets in Brazil, when cell phones with IDTV are available to population. Thus, some differences between IDTV for residences and for cell phones can be noticed. IDTV standard adopted in Brazil calls full-seg the fixed devices like set-top-box, and one-seg, devices like cell phones, miniTVs, PDAs, etc. In residences, the IDTV is used by all residents while in the cell phone it is normally used by only one user, the owner of the device. Another characteristic is the size of the display. In residences, the IDTV television sets have screens bigger than 30”, where is possible to have a more flexible development, presentation and displaying of the content. However, cell phones screens are smaller than 10" requiring a higher effort in development to display the content on the screen avoiding image pollution and confusion to the user. An exceptional characteristic in this environment is that IDTV for cell phones can be seen anywhere and anytime. On the other hand, IDTV viewing period in residences can be longer than in cell phones which are used in situations of waiting and displacement. IDTV in cell phones can use already existent 2G/3G net architecture, and 4G in the future, as a return channel, making interactivity possible in this environment before occurring in IDTV. The middleware adopted in Brazil has national technology and is called Ginga. Ginga-NCL and Ginga-J declarative and imperative portions of the middleware are necessary for full-
  • seg devices. For one-seg devices, only Ginga-NCL declarative portion is required. There is a reference implementation of the middleware for full-seg devices. For one-seg devices, this reference implementation is not available yet, but are working in this middleware development, as PUC-RIO and UFES (Symbian e Android). [11, 12, 13] The users of these devices need special attention due to current characteristics of this environment like processing power, storage capacity and battery. III. RELATED WORK There are many works involving recommendation systems for IDTV for set-top-box and more recently for portable devices. This section presents two recent works about recommendation systems for IDTV. In [5] the recommendation system fits the systems with content-based filtering category, using text mining. The system uses a simple interface with the user and accepts a natural language as text entry as well as four values which reflect user preferences for comedy, action, horror and erotic. First, the system extracts texts and then searches for emotions in the text and the distances among themes are calculated. Finally, an index is calculated for each entry and a list of programs organized by this index is returned. In [3] the main aim of the system is substituting the common content by a personalized and adapted content in a more attractive way for the user. Therefore, this system accepts and allows TV reception either through broadcast, or multimedia streaming. The system uses explicit collection – when using for the first time it is necessary to inform the preferences – and also implicit collection – user’s actions in the device are monitored, stored and sent to the server. The personalized content– chosen based on preferences – is sent to the user’s portable device by the Server in order to be previously stored before being exhibited. The ZapTV [4] developed for DVB-H standard allows the user to create his own content, offering aggregated value services as multimodal access (Web and Cell phones), return channel, video note, personalized sharing and distribution of content. Besides the technology provided by DVB-H, ZapTV comprehends other technologies as TV-Anytime, Technologies emerging from Web 2.0 and involved in the Semantic Web. The main functionalities of ZapTV include a social net, personalized content broadcasting (implicit or explicit recommendation), thematic channels diffusion planning (age-group, genre or specific theme), client application and transmission of the electronic programming guide. ZapTV seeks to improve the recommendation using an intelligent personalization mechanism which matches information filtering with semantic logic processes and it was based on the principles of participation and sharing between Web 2.0 users, so that the creation, sharing, classification and note of content make the search for content easier. Our recommendation system is in the portable device, and the inclusion of servers in Brazilian IDTV architecture for cell phones is not necessary to provide recommendation and, consequently the need for remote communication is also not necessary, avoiding that the user pay for the data traffic in the net in order to receive recommendation or send his data, and thus, protecting the user’s data privacy. IV. CONTENT-BASED FILTERING Content-Based Filtering (CBF) uses the content attributes to describe the content of the items and then calculate the similarity. This approach does not depend on other users’ evaluation about the items. CBF is an information recovery technique which bases its forecast on the fact that previous preferences of the users are reliable indicators for future behavior. In order to formulate recommendations, a variety of algorithms has been proposed to evaluate the content of documents and find regularities. Some of these algorithms operate with classification knowledge and others operate with the problem of regression. Some of the problems and limitations found in systems using CBF are super specialization, the problem of the new user and the analyses of limited content. [7, 6, 14] V. RECOMMENDER SYSTEM Our recommendation system aims to facilitate the IDTV user’s routine by interacting with a simple interface which provides content of preference without spending so much time to find it. The process starts when the user turns on the TV in the cell phone. The user history data collected is submitted to information filtering based on content in order to find the user’s profile. Data resulting from this process are formatted. The user profile is stored in a database with the date and time of the generation. With the user profile updated it is possible to look in the EPG for compatible TV programs and which are going to be transmitted around the current time, providing a list of these programs. This list is also stored in a data base with the date and time of generation. The recommendations are presented to the user and those required are stored with the user history. During the time the IDTV for cell phone is turned on, all programs viewed by the user are stored in the database which has the user history. This process is repeated every time the user turns on the TV. Ginga-NCL middleware has a layer for resident applications responsible for exhibition, other layer for the common center responsible for offering several services and a last layer regarding the protocols stack. The recommendation system is considered as an element in Ginga-NCL architecture, in Ginga Common Core, due to the need to continue using data locally and also use Tunner libraries – in order to obtain information about the channels tune – ESInformation – in order to obtain information about EIT table generating the EPG – and Context Manager – to obtain system information. As GINGA-NCL middleware is mandatory in Brazil for portable devices, the recommendation system was planned,
  • designed and modeled according to Brazilian rules which refer to portable devices, thus meeting these devices needs. More details on our system can be obtained in. [15] VI. TESTS For the tests we used the data corresponding to TV viewing and program schedule. These data were provided by IBOPE which is a Brazilian multinational private equity firms and a leading market research in Latin America. 67 years ago to IBOPE provides a wide range of information and media studies, public opinion, voting intention, consumption, brand and market behavior. In the following subsections the characteristics of these data and the tests will be detailed. [16] A. Characteristics of Residences Data that contain information provided IBOPE EPG (TV programming), history of the user's view (what the viewer saw) and also the socioeconomic information. All these data were separated and stored in MySQL database the data correspond to 15 days of programming and monitoring of six Brazilian households with TV programming Open. These households were monitored every minute, and each individual was also monitored separately. TABLE I NUMBER OF INDIVIDUALS BY RESIDENCE Residence Individuals TVs 1 2 1 2 3 1 3 3 2 4 2 2 5 2 1 6 3 2 B. Characteristics of Date The data used for these tests have undergone a process of manual adjustment. For each of the algorithms used, was a necessary pre-processing for correct use and analysis. Subsections C, D, E and F detail the composition of these data. C. EPG The EPG (Electronic Program Guide) is composed of 15 TXT files called programming files, one for each day (05/03/2008 to 19/03/2008) with a grid of 10 TV stations Open, starting at 00:00:00 and ends at 05:59:00. After understanding the files that make up the EPG, the data were copied from the archives of programming a spreadsheet BrOffice and then was done cleaning up unnecessary data. We noticed some inconsistencies in schedules, which were immediately corrected so that future analysis will not generate erroneous results. This was repeated for each of the 15 programming files, generating a single spreadsheet containing the entire 15 days of EPG. Was added to these data the day of week and duration of the program. The EPG, this step is not complete, missing the genre and subgenre of each program. Searched for it on the official websites of the gender of each station broadcast programs and then identified according to the Brazilian standard ABNT NBR 15603-3:2007, Annex C, "gender descriptor in the descriptor content." [17] A new table was created, identical to the EPG table, but with added fields with the names of genre, to be used with the technique of the cosine. These fields were populated with 0 or 1 depending on the program or not fit in that genre, becoming a matrix. D. User History Historical data viewing of users are needed for the discovery of these preferences. In the context of digital TV that we are considering, these data are collected and stored implicitly. Spreadsheets sent by tuning IBOPE, which contains user data, were modified so that filtering techniques based on content could be applied. The data were then separated by households and although some homes have more than one TV in these households it was noticed that there is no record of monitoring more than one TV at the same time and therefore it is considered that the household has only one TV. Data were also formatted: date in yyyy-mm-dd, time in hh:mm:ss format TV and 00X. The resulting sheets were converted to CSV files that were then inserted in MySQL were also added to user data, information for day of week, time of day and duration of display. E. Methodology We simulate the Content Filtering Based on using two different techniques, the Apriori and cosine, both using as a target attribute to gender. In the case of Apriori, we apply the settings shown in Figure 1. Then start the simulation for Apriori. For each household, the process was the same on the first day to generate recommendations for the second day, the second day, based on what was seen the day before and the present day, to generate recommendations for the third day, and so on. First we opened the CSV file corresponding to the X home and day 1. After convert some attributes String to Numeric Nominal and others for Nominal (applying filters). Then the executable Apriori and ultimately save the output. With the data saved, it was possible to assess whether the day after, someone from the household attended any of the genera found by Apriori. To find the cosine first and save the profile, then calculate the distance of the cosine, cosine and the standard itself and finally found the right answers. The process is iterative and is performed for each day and each household. F. Apriori e Cosine The algorithms of association techniques identify associations between data records that are somehow related. The basic premise is evidence on the presence of others in the same transaction, to determine what things are related. View slide
  • Moreover, we also calculated the average percentage of correct answers for the number of recommendations generated using the following formula: (2) Figure 2 and Figure 3 show the chart with the average achieved for the percentages calculated for each hit home for the techniques of the Cosine and Apriori. Households 2, 4 and 6 had the best results for the cosine and the Apriori households 4:06. As can be seen in Figure 4, which presents the comparison chart between the two techniques in general, the cosine has outperformed over the Apriori. Figure 1. Parameters used in Apriori. The association rules interconnect objects in an attempt to expose patterns and trends. The discovery of associations must show both associations as trivial associations not trivial. The Apriori algorithm is often used to mine association rules. Apriori can work with a high number of attributes, generating various combinations between them and performing successive searches across the database, maintaining optimum performance in terms of processing time. The algorithm tries to find all the relevant association rules between items, which has the form X (history) ==> Y (consequent). If x% of transactions that contain X also contain Y, then x% represents the factor of trust (under the rule of confidence). The support factor is a measure representing x% of cases in which X and Y occur simultaneously over the total number of records (often). [18] The cosine is a measure of similarity, a metric that can be applied to find out if an item has a correlation or not with the user profile. A binary vector is a set of two elements, x and y. In an n-dimensional space, where n is the number of items in the vector. You can therefore calculate the cosine between the vectors, measured as similarity between the user profile and its history. The similarity is high when the value of cosine is high, the closer to 1, the greater the similarity. [19] Figure 2. Parameters used in Cosine. Figure 3. Parameters used in Apriori. VII. RESULTS For all households were made in excel spreadsheets to account for the percentage of success of each technique to each household. As the number of recommendations were generated in May, then the basic formula for calculating the percentage of correct answers was used: (1) Figure 4. Comparison between means of Apriori and Cosine. View slide
  • VIII. CONCLUSION During the tests, we observed some peculiarities. Our system recommends content based on the kinds of programs, and our analysis were made according to that parameter. With the Apriori algorithm, the format of the data is already collected correctly for use. For cosine, the EPG needs to be changed to an array before starting the discovery process profiles and recommendations. The Apriori is able to mine only the user viewing history, discovering your profile from the rules. To select the programs to be recommended, another technique should be used. The cosine can do both. However, Apriori can discover more features in the user history, for example, "the user stands in front of the TV more often at night, he enjoys watching movies and watching TV more frequently in the second." The cosine cannot find these features, but can reach our goal. To find similar patterns in association rules, it is necessary to use more complex queries to the bank. The output from the Apriori must be crafted to generate the correct profile of the user, ie, the rules should be interpreted, which in terms of implementation becomes somewhat cumbersome. The cosine output is more readable, its result goes straight to the intended goal, allowing the output to be used without the need for a post-treatment. In relation to entry to the Apriori there is no need of treatment, since the data will be used the way they are collected. But for the cosine, where the EPG is updated, the table containing the matrix of the EPG should be modified according to the new EPG, becoming somewhat laborious. Then simulated with both techniques the process of delivery and acceptance of recommendations by calculating the percentage of correct and generating graphics. The profile of genres found by both algorithms are similar. Although both techniques to cover the needs of the system, the cosine is one that can be better utilized. On a desktop, as was the case of our tests, the return of calculating the cosine is faster in relation to the return of the Apriori association rules. However, further studies on the consumption of processing of these algorithms in a cell with TVDI is not yet possible in Brazil. The time that the whole process of recommendation takes to complete varies according to the technique of customization to be used. In our tests and simulations, the cosine ends the process before the Apriori. Studies show that although both algorithms meet our needs, these two techniques the Cosine can be better worked in the recommendation system for TVDPI. ACKNOWLEDGMENT We thank IBOPE for providing real data about the electronic program guide and also the viewer’s behavior data from March, 05, 2008 to March, 19, 2008. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] Avila, P. M. 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