Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities †
Abstract
:1. Introduction
2. Literature Review
2.1. CTS
2.2. CF
3. Computational Approach
3.1. Weight Calculation
3.1.1. Tag-Based Weight
3.1.2. Time-Based Weight
3.1.3. Hybrid Weight
3.1.4. Specific Example
- Tag-Based Weight
- 2.
- Time-Based Weight
- 3.
- Hybrid Weight
3.2. User Similarity Calculation
3.3. Resource Preference Generation
3.4. Algorithm Code
Algorithm 1. Algorithm code. |
rawlog = load(‘MarTraLog.txt’); rawtag = load(‘MarTraTag.txt’); rawtime = load(‘MarTraTime.txt’); Test01 = load(‘MarTraTest.txt’); % Iteration Experiment Start for hi = 1:11 rawmix = rawtag*(1–0.1*(hi-1)) + rawtime*0.1*(hi-1); for i = 1:id for j = 1:id simmix(i,j)=rawmix(i,:)*rawmix(j,:)’/(norm(rawmix(i,:))*norm(rawmix(j,:))); end end % Neighbor scale parameter = [3 6 9 12 15 18 21 24 27 30]; for j = 1:length(parameter) k = parameter(j); for i = 1:id [m n] = sort(simmix(i,:), ‘descend’); index = n(2:k + 1); Preefer_mat(i,:) = simmix(i,index)*rawmix(index,:)/k; Result{j} = prefer_mat; end end % Evaluation Part for j = 1:length(parameter) prefer01 = Result{j}; for i = 1:size(prefer01,1) [pm pn] = sort(prefer01(i,:),’descend’); index = pn(1:10); Recall01(i,1) = length(find(Test01(i,index) = =1))/length(find(Test01(i,:)~ = 0)); Precision01(i,1) = length(find(Test01(i,index) = =1))/10; end Recall_001(:,j + 10*(hi-1)) = Recall01; Precision_001(:,j + 10*(hi-1)) = Precision01; end end |
4. Experiment
4.1. Dataset and Experimental Procedure
4.2. Evaluation Metrics
4.3. Results
4.3.1. Results of Margarin
4.3.2. Results of Delicious
5. Conclusions and Further Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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R1 | R2 | R3 | R4 | R5 | |
---|---|---|---|---|---|
U1 | Tag1, Tag2 (May 15) | Tag 1, Tag 3, Tag 4 (May 6) | Tag1, Tag4, Tag5 (May 11) | ||
U2 | Tag4 (May 15) | Tag4, Tag6 (May 1) | |||
U3 | Tag1, Tag7 (May 15) | Tag 8 (May 11) | Tag 4 (May 6) | Tag1, Tag 7 (May 6) |
Tag | R1 | R2 | R3 | R4 | R5 |
---|---|---|---|---|---|
U1 | 0.5 | 0 | 0.75 | 0.75 | 0 |
U2 | 0 | 0.67 | 0 | 1.0 | 0 |
U3 | 0.67 | 0 | 0.17 | 0.17 | 0.67 |
Time | R1 | R2 | R3 | R4 | R5 |
---|---|---|---|---|---|
U1 | 1.0 | 0 | 0.25 | 0.5 | 0 |
U2 | 0 | 1.0 | 0 | 0.25 | 0 |
U3 | 1.0 | 0 | 0.5 | 0.25 | 0.25 |
Hybrid | R1 | R2 | R3 | R4 | R5 |
---|---|---|---|---|---|
U1 | 0.75 | 0 | 0.5 | 0.625 | 0 |
U2 | 0 | 0.835 | 0 | 0.625 | 0 |
U3 | 0.835 | 0 | 0.335 | 0.21 | 0.46 |
Symbol | Meaning |
---|---|
u | User |
v | Neighbor User |
r | Resource |
R | Set of Resources |
i | A Certain User i |
Wtag(u,r) | Weight from Tags by User ‘u’ for Resource ‘r’ |
Wtime(u,r) | Temporal Weight of User ‘u’ towards Resource ‘r’ |
Whybrid(u,r) | Combined Tag-Time Weight for User ‘u’ and Resource ‘r’ |
Wi(u,r) | Composite Weight Profile of User ‘u’ for Resource ‘r’ |
Wi(v,r) | Each Weight Vector of Neighbor User ‘v’ to Resource ‘r’ |
λ | Parameter for Balancing Tag-Based and Time-Based Weights |
simi(u,v) | Similarity Score Between User ‘u’ and User ‘v’ |
Stag, Stime, Shybrid | Tag-Based, Time-Based, and Hybrid Similarities, respectively |
KNN(u) | Set of ‘k’ Nearest Neighbors of User ‘u’ |
Score(u,r) | Predicted Preference Score of User ‘u’ for Resource ‘r’ |
t | Relative Time Point Value |
ti | Tagging Day |
tl | Last Tagging Day |
tf | First Tagging Day |
hlt | Half-Life for Each User |
Data | Number of Resources | Number of Tags | Number of Bookmarks |
---|---|---|---|
Margarin | 15,765 | 11,065 | 18,850 |
Delicious | 43,028 | 9383 | 104,687 |
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Jo, H.; Hong, J.-h.; Choeh, J.Y. Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities. Appl. Sci. 2023, 13, 10528. https://doi.org/10.3390/app131810528
Jo H, Hong J-h, Choeh JY. Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities. Applied Sciences. 2023; 13(18):10528. https://doi.org/10.3390/app131810528
Chicago/Turabian StyleJo, Hyeon, Jong-hyun Hong, and Joon Yeon Choeh. 2023. "Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities" Applied Sciences 13, no. 18: 10528. https://doi.org/10.3390/app131810528
APA StyleJo, H., Hong, J.-h., & Choeh, J. Y. (2023). Improving Collaborative Filtering Recommendations with Tag and Time Integration in Virtual Online Communities. Applied Sciences, 13(18), 10528. https://doi.org/10.3390/app131810528