Retrieval,Recommendation And Feedback Learning For Massive Video Program

With the improvement of network and digital multimedia technology, it makes people increasingly difficult to get useful information from the explosive growth of digital products and obtain services from the rapid development of Internet application. Therefore, how to effectively discover, retrieve and process with massive data becomes a problem worthy of study.This dissertation focuses on the researches in the following three issues:1) The multimedia retrieval methods are shifted from key-words based ones to example-based ones. The video clip is employed as an input for finding similar video clips in huge datasets. Based on bag of video words, the traditional retrieval method for video ignored ordinal association among video frames. There’s still room for enhancing the system performance. How to measure the similarity of the time series for video frames and maintain acceptable retrieval speed is worthy of future study.2) Collaborative filtering is widely used in the online e-commerce and proved to be one of popular methods in the recommender system. However, the effect of collaborative filtering is limited by several problems. Due to the lack of user selection information, recent researches show that items in the long tail turn to be ignored by traditional collaborative filtering algorithm. By the same token, the cold-start of the recommender system also becomes a difficult problem for collaborative filtering method. It is particularly important to find similarity which is based on the inherent relationship between items, to overcome problems with missing information.3) In some actual scenes, the recommended algorithm may be based on explicit feedback(rating scores etc.). However, the rating collection process needs some help from the user to collect information. This will impact the user’s experience, and lead to insufficient information problem in recommender system. Implicit feedback such as the time spent on website, how soon the user skipped the song, and the ordinal association of selected items have proved to be useful in recommender systems. How to make use of implicit information to revise the result of the recommendation is worthy of further investigation.For these problems, the main research work and innovations in this dissertation are concentrated on three main parts:1. To improve the search efficiency for large-scale video database, we describe an approach to video retrieval for copy detection. Firstly, an ordered list of global frame features with systematic sampling is extracted from the video clip. The global features then are hashed into time series, which is represented for the video clip. In the retrieval process, the elements of time series are used as video words. The category and dispersion rate of common feature can be calculated by inverted index, which is used to filter unrelated candidates. The distance of time series can be calculated by dynamic time warping with Jaccard distance. Cheap-to-compute low bound is also used to prune off unpromising candidates in the DTW computing processes. The experimental results indicate that the proposed approach achieves the same performance with1/3time consumption compared with original DTW algorithm, and a better ratio of score and time compared with the result of other methods in MUSCLE VCD2007dataset.2. To solve the data sparsity problem in recommender system, we introduce an approach using ontology-based similarity to estimate missing values in the user rating matrix, and improve the effect of recommendation for items in the long tail and the cold start problem. With movie domain, for example, the method find similar candidates by the similarity of features in movie ontology. The missing rating score is predicted by the rating score for ontology similar candidates set. With the filling rating matrix, the PureSVD method could get better performance in recall metric. Experiments using Hetrec’11dataset were carried out to evaluate the proposed methods with Top-N recall metrics. Compared with state-of-the-art approaches(TopPop, Neighborhood recommendation and PureSVD), the proposed method achieves24%～30%better recall rate for average, and2to5times better recall rate when applied to the long-tail situation. It could also well deal with the new item cold start problem.3. To solve the problem of insufficient information based on explicit feedback, we proposed a recommendation algorithm considering implicit feedback to produce a better recommended list. The implicit feedback of users is used as a0-1user-item rating matrix and put into the directed correlation graph. Time window is also used to limit the impact range of implicit feedback in graph. The authority and hub values can be calculated by HITS iteration algorithm, which are used as results for recommendation from implicit feedback. The final recommended list is the result of both explicit feedback and implicit feedback. Experimental results on MovieLens datasets show that the implicit feedback can effectively compensate for the shortcomings of explicit feedback. In the proposed algorithm, the macro-average degree of agreement (DoA) could achieve90%, which is more accurate than the PureSVD and ItemRank based on explicit feedback.Part of research results mentioned above has been applied into projects as follows:the National Key Technologies R&D Program of China "The Architecture, Key Technologies and Test Specifications of Enhanced Search System"(No.2011BAH11B01), the Key Programs of the Chinese Academy of Sciences "A New TV Commerce Complex Format Application Demonstration"(2012BAH73F02).