Incorporating Time Dynamics and Implicit Feedback into Music Recommender Systems

A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

“…In the second, Pacula's method was used to calculate the ratings from the simple play count, without considering sessions. Basic and matrix factorization CF (1) P : � p i,j , P ϵ M n×m (N), (2) S : � s i,j , S ϵ M n×m (N) (3) NS : � ns i,j , NS ϵ M n×m (N) (4) Set α value (5) for i � 1 to n do (6) for j � 1 to m do (7) Freq i,j � α(s i,j / 􏽐 m j�1 s i,j ) + (1 − α)(ns i,j / 􏽐 m j�1 ns i,j ) (8) end for (9) end for (10) for i � 1 to n do (11) T � sequence (Freq i,j ) k′ 􏽮 􏽯 ∀ j |p i,j > 0 ∧ (Freq i,j ) k′ > (Freq i,j ) k ′ +1 (12) S(i) � sequence Freq k′ (i) 􏼈 􏼉 � T (13) for j � 1 to m do (14) Set k value |Freq k (i) � Freq i,j (15) r i,j � 4 (1 − 􏽐 k−1 k′�1 Freq k′ (i)) (16) end for (17) end for (18)…”

“…e number of K neighbors was set to 5 since it provided the best results in the experiments. Although it may seem insufficient to make the predictions, this number of neighbors has been successfully used in other work in the same field of application [8]. In addition, two matrix factorization methods were applied, the basic technique and a variant called biased matrix factorization that incorporates user and item regularization parameters.…”

“…is means that the number of ratings obtained from the users is fewer than the number of ratings needed for prediction [7]. Matrix factorization approaches can be used to deal with this problem, but these methods have some disadvantages, such as the cost of building the models and the loss of information resulting from the dimensionality reduction, and these are not always compensated with a significant improvement of results [8]. us, in many cases, it is more effective to resort to implicit ratings that can be obtained from the time that users spend examining the items or from other data stored in log files; although in this case, it must be assumed that preferences derived from this information are usually not as reliable as the explicit ones.…”

Time-Aware Recommender System for E-Commerce Applications

“…Many researchers investigated the use of decay function in RSs [11][12][13][14][15][16]. In [17]authors produced a simulated rating by aggregating implicit feedback dataset with time dynamics using the decay function similar. The experimental results demonstrate the effectiveness of their proposed approach.…”

“…From the literature, we observe that time is mostly used in the media domain [11,[13][14][15][16][17][18]and learning domain [21][22][23][24] [8,19]. But it is rarely used in the e-commerce domain.…”