## 4 Results
### Temporal Models and Recommendation Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rec. Technique** & **Time-Approack/Algorithms** & **Temporal Factor** & **Shortonings** & **No. of Studies** & **References** \\ \hline \multirow{4}{*}{Neighborhood} & \multirow{2}{*}{Time-dept/ weight fun.} & \multirow{2}{*}{User prefer, item popularity} & This incorporates decay func in the similarity computation and rating prediction to reduce the influence of older observations over time. The challenge is the selection of appropriate rate of forgetting so that it corresponds to the rate and type of change. & \\ \cline{2-6}  & & Time-dept/ & User prefer, item popularity & Window size is critical: if it is too long the system is sensitive to changes and became unstable and undermined of otherwise. & 5 & [100, 123, 124, 125, 126] \\ \cline{2-6}  & \multirow{2}{*}{Time-indept/ window-based} & \multirow{2}{*}{User prefer.} & The method used only the data that is similar to the current period to make predictions, which makes it more prone to cold start and data sparsity challenges. & 3 & [8, 3, 6] \\ \hline \multirow{4}{*}{Facetor Models} & \multirow{2}{*}{Time-dept/TMF} & \multirow{2}{*}{User prefer./ item popularity/rating} & The approach utilized a temporal factorization models by studying changes in a transition matrix corresponding to user preferences for different sliding windows. & 10 & [5, 22, 23, 74, 8, 120, 127, 128, 129, 130] \\ \cline{2-6}  & \multirow{2}{*}{Time-dept/ Tensor Decom.} & \multirow{2}{*}{User prefer.} & The dynamic aspect of time information was not considered which makes it less accurate in addressing drift problems & 5 & [73, 74, 129, 131] \\ \hline \multirow{4}{*}{Long/Short-term} & \multirow{2}{*}{Time-dept/ Long-vs-short-term} & \multirow{2}{*}{User prefer.} & Time was used as additional information to address the change of user preference. However, the change point of user preference is required to be able to adapt to changes appropriately and timely. & 9 & [8, 14, 6, 6, 9, 83, 84, 90, 131, 132] \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & \multirow{2}{*}{Time-dept/ long-vs-short-term} & \multirow{2}{*}{User prefer.} & The whole time is divided into static periods of time as short-terms which is challenging as user preference is dynamic as time increases continuously. & 6 & [36, 45, 75, 102, 133, 134] \\ \hline \multirow{4}{*}{Data stream} & \multirow{2}{*}{Time-dept/ strong mining} & \multirow{2}{*}{User prefer.} & The models were based on continuous learning and adapting the user profiles in a given session, without applied change detection. & 4 & [2, 3, 65, 111] \\ \cline{2-6}  & & & & & \\ \hline Euclidean embedding & Time-dept/ Euclidean distance & \multirow{2}{*}{User prefer.} & Euclidean embedding was proven to be more effective, but insufficient in producing accurate drift measures. & 3 & [2, 91, 92] \\ \hline \multirow{4}{*}{Hybrid methods} & \multirow{2}{*}{Time-indept/ time-biased KNN} & \multirow{2}{*}{User prefer.} & Managing large number of models increases computational cost and often result in slower adaptation to change environments. & 2 & [5, 135] \\ \cline{1-1} \cline{4-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Temporal CF Algorithms for Recommender System.