Browsing by Author "Rachsuda Jiamthapthaksin"
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    Alternating Least Squares with Incremental Learning Bias
    ( 2015) Aung, Than Htike ; Rachsuda Jiamthapthaksin
    Recommender systems provide personalized suggestions for every individual user in the system. Many recommender systems use collaborative filtering approach in which the system collects and analyzes users' past behaviors, activities or preferences to produce high quality recommendations for the users. Among various collaborative recommendation techniques, model-based approaches are more scalable than memory-based approaches for large scale data sets in spite of large offline computation and difficulty to update the model in real time. In this paper, we introduce Alternating Least Squares with Incremental Learning Bias (ALS++) algorithm to improve over existing matrix factorization algorithms. These learning biases are treated as additional dimensions in our algorithm rather than as additional weights. As the learning process begins after regularized matrix factorization, the algorithm can update incrementally over the preference changes of the data set in constant time without rebuilding the new model again. We set up two different experiments using three different data sets to measure the performance of our new algorithm.
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    Clustering Analysis on Alumni Data Using Abandoned and Reborn Particle Swarm Optimization
    ( 2016-02) Paulus Mudjihartono ; Thitipong Tanprasert ; Rachsuda Jiamthapthaksin
    Alumni data is one of the most important data that university management uses for developing the learning process decisions. This paper applies the idea of Abandoned and Reborn PSO (AR-PSO) to convert a clustering problem into the optimization form with an objective function to minimize the ugliness of the desired clusters. This algorithm of Clustering using AR-PSO (CAR-PSO) is slightly adapted to the cluster problem domain. The generated clusters need to be examined to decide if they are acceptable. There are three evaluations; the closeness, the separation and the purity. Finally, the experiment results show that the CAR-PSO is comparable with &-means in both types of alumni data while leaving the other two clustering algorithms.
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    Dimension Independent Cosine Similarity for Collaborative Filtering using MapReduce
    ( 2016-02) Shen, Fei ; Rachsuda Jiamthapthaksin
    DIMSUM, an efficient and accurate all-pair similarity algorithm for real-world large scale dataset, tackles shuffle size problem of several similarity measures using MapReduce. The algorithm uses a sampling technique to reduce `power items' and preserves similarities. This paper presents an improved algorithm DIMSUM+ with a complex sampling technique to enhance DIMSUM so that it is able to further reduce `power users'. The algorithm generates k-nearest-neighbor matrix that are used in collaborative based Recommender systems. The evaluations of algorithm on MovieLens dataset with 1 million movie ratings and Yahoo! Music dataset with 700 million song ratings show significant improvement that DIMSUM+ outperforms DIMSUM at least 1.4x faster.
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    A Framework of Incorporating Thai Social Networking Data in Online Marketing Survey
    ( 2016) Nitipan Ratanasawadwat ; Rachsuda Jiamthapthaksin ; Than Htike Aung ; Assumption University. Vincent Mary School of Science and Technology
    With the introduction of high-speed Internet and smartphones at an affordable price range, many Thai citizens possess smartphones and utilize them as part of their daily life activities. The high mobile phones penetration and social networking usage is conductive to new approaches in performing marketing survey. This research proposes a framework that automatically incorporates Thai social networking data with online marketing survey for marketing analysis. In particular, it provides online marketing survey to a respondent, and automatically associates his/her Facebook data for further analysis. The benefits of the framework includes reducing manpower required in traditional surveys, offers easy accessibility to the respondents, automatically retrieving social networking data, and associating them the online questionnaires of each respondent for further marketing analysis.
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    A System for Popular Thai Slang Extraction from Social Media Content with N-Gram Based Tokenization (KST 2016)
    ( 2016-02) Rachsuda Jiamthapthaksin ; Pisal Setthawong ; Nitipan Ratanasawadwat ; Assumption University. Martin de Tours School of Management and Economics
    With increased penetration of smart devices and internet connectivity, many Thais are more readily engaged in social media, online forums, and chat groups. As there is an increased consumption of social media content, there is a shift from the consumption of traditional medias in which formal language are used regularly such as broadcast and traditional print medias. Social media posts are a reflection of the trend, where posts usually made by younger generations usually involve communication in slang and non-formal language which is not typically available in formalized dictionaries. As the Thai population like to follow trends, one of behaviors of that many Thai social media users engage in, is to follow the latest popular social media trends in slang and word usage. As slang are changed and evolved over time, it is usually useful to have an online mining tool in which could capture the trends of emerging and popular slang. This paper proposes an approach that extracts popular Thai slang by comparing social media posts and utilizing tokenization, a dictionary based approach to extract unknown words, before expanding it by using n-gram approach to figure what are currently trending and popular slang words.