Handbook of Multimedia for Digital Entertainment and Arts- P2

Handbook of Multimedia for Digital Entertainment and Arts- P2: The advances in computer entertainment, multi-player and online games,technology-enabled art, culture and performance have created a new form of entertainmentand art, which attracts and absorbs their participants. The fantastic successof this new field has influenced the development of the new digital entertainmentindustry and related products and services, which has impacted every aspect of ourlives.
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Handbook of Multimedia for Digital Entertainment and Arts- P216 G. Lekakos et al.Fig. 4 Method selection in MoReFig. 5 Ranked list of movie recommendationsRecommendation AlgorithmsPure Collaborative FilteringOur collaborative filtering engine applies the typical neighbourhood-basedalgorithm [8], divided into three steps: (a) computation of similarities betweenthe target and the remaining of the users, (b) neighborhood development and (c)computation of prediction based on weighted average of the neighbors’ ratings onthe target item.1 Personalized Movie Recommendation 17 For the first step, as formula 1 illustrates, the Pearson correlation coefficient isused. P Xi X Yi Y i r D rP (1) 2P 2 Xi X Yi Y i iwhere Xi and Yi are the ratings of users X and Y for movie I , while X, Y referto the mean values of the available ratings for the users X and Y . However, inthe MoRe implementation we used formula 2, given below, which is equivalent toformula 1 but it computes similarities faster since it does not need to compute themean rating values. n represents the number of commonly rated movies by users Xand Y . P P P n XiYi Xi Y i i i i rDs  Ã2 s  Ã2 (2) P 2 P P 2 P n Xi Xi n Yi Yi i i i iNote that in the above formulas if either user has evaluated all movies with identicalratings the result is a “divide by zero” error and therefore we decided to ignore userswith such ratings. In addition, we devaluate the contribution of neighbors with lessthan 50 commonly rated movies by applying a significance weight of n=50 , wheren is the number of ratings in common [32]. At the neighborhood development step of the collaborative filtering processwe select neighbors with positive correlation to the target user. In order to increasethe accuracy of the recommendations, prediction for a movie is produced only if theneighbourhood consists of at least 5 neighbors. To compute an arithmetic prediction for a movie, the weighted average of allneighbors’ ratings is computed using formula 3. P .J i N J /rKJ J 2Neighbours N Ki D K C P (3) jrKJ j J Nwhere Ki is the prediction for movie i , K is the average mean of target user’s ratings, NJi is the rating of neighbour J for the movie i , J is the average mean of neighbour 0J s ratings and rKJ is the Pearson correlation measure for the target user and herneighbor J .Pure Content-Based FilteringIn the content-based prediction we consider as features all movie contributors (cast,directors, writers, and producers), the genre, and the plot words. Features that appear18 G. Lekakos et al.in only one movie are ignored. Each movie is represented by a vector, the length ofwhich is equal to the number of non-unique features of all available movies. Theelements of the vector state the existence or non-existence (Boolean) of a specificfeature in the description of the movie. To calculate the similarity of two movies, we use the cosine similarity measurecomputed in formula 4. ai and bi are the values of the i -th elements of vectorsE Ea and b. P ai bi E D E E a b i cos.E ; b/ a D rP rP (4) E kE k kbk a ai2 bi2 ...