This is our official implementation for the paper [url]:
Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren and Tat-Seng Chua, Fast Matrix Factorization with Non-Uniform Weights on Missing Data, TNNLS.
It is an extended work of the SIGIR'16 paper "Fast Matrix Factorization for Online Recommendation with Implicit Feedback".
If you use the codes, please cite our paper . Thanks!
The core model is src/algorithms/MF_extALS.java.
A contrast model based on sparse SVD is svd.py.
We exhibit a small dataset data/yelp.rating, in which the columns indicate the user_id, item_id, rating score and rating time, respectively.
Run the main function in src/main/main_MF.java.
The outputs with default settings are as below:
HoldOne out splitting.
Sort items for each user.[00:00:00.470]
Generate rating matrices.[00:00:01.056]
Data data/yelp.rating
#Users 25677
#Items 25815
#Ratings 672765 (train), 25677(test)
extALS: showProgress=false, factors=64, maxIter=500, reg=0.010000, w0=10.00, alpha=0.75
====================================================
Popularity <hr, ndcg, prec>: 0.0705 0.0176 0.0062 [00:00:02.233]
training size: 662851755
user: 25677, item: 25815
relates: 1
Iter=0 [00:00:06.892] [-]loss: 261836.1007 [00:00:00.338]
Iter=1 [00:00:04.821] [-]loss: 96839.4645 [00:00:00.496]
Iter=2 [00:00:04.554] [-]loss: 67288.0789 [00:00:00.242]
Iter=3 [00:00:04.531] [-]loss: 54636.3216 [00:00:00.225]
Iter=4 [00:00:04.514] [-]loss: 47446.8384 [00:00:00.224]
Iter=5 [00:00:04.506] [-]loss: 42775.1669 [00:00:00.222]
Iter=6 [00:00:04.485] [-]loss: 39475.7438 [00:00:00.227]
Iter=7 [00:00:04.505] [-]loss: 37002.0581 [00:00:00.226]
Iter=8 [00:00:04.591] [-]loss: 35060.2324 [00:00:00.229]
Iter=9 [00:00:04.546] [-]loss: 33479.9425 [00:00:00.229]
Iter=10 [00:00:04.559] [-]loss: 32157.4193 [00:00:00.228]
Iter=11 [00:00:04.613] [-]loss: 31027.1596 [00:00:00.231]
Iter=12 [00:00:04.572] [-]loss: 30046.2365 [00:00:00.233]
Iter=13 [00:00:04.538] [-]loss: 29185.1848 [00:00:00.224]
Iter=14 [00:00:04.557] [-]loss: 28422.7083 [00:00:00.237]
Iter=15 [00:00:04.564] [-]loss: 27742.7244 [00:00:00.225]
Iter=16 [00:00:04.547] [-]loss: 27132.6735 [00:00:00.226]
Iter=17 [00:00:04.588] [-]loss: 26582.4973 [00:00:00.228]
Iter=18 [00:00:04.566] [-]loss: 26083.9762 [00:00:00.225]
Iter=19 [00:00:04.550] [-]loss: 25630.2859 [00:00:00.228]
Iter=20 [00:00:04.574] [-]loss: 25215.7115 [00:00:00.224]
Iter=21 [00:00:04.535] [-]loss: 24835.4411 [00:00:00.227]
Iter=22 [00:00:04.506] [-]loss: 24485.4029 [00:00:00.225]
Iter=23 [00:00:04.510] [-]loss: 24162.1307 [00:00:00.223]
Iter=24 [00:00:04.523] [-]loss: 23862.6545 [00:00:00.226]
Iter=25 [00:00:04.503] [-]loss: 23584.4155 [00:00:00.228]
Iter=26 [00:00:04.514] [-]loss: 23325.1992 [00:00:00.229]
Iter=27 [00:00:04.566] [-]loss: 23083.0798 [00:00:00.227]
Iter=28 [00:00:04.603] [-]loss: 22856.3758 [00:00:00.225]
Iter=29 [00:00:04.632] [-]loss: 22643.6149 [00:00:00.226]
Iter=30 [00:00:04.524] [-]loss: 22443.5050 [00:00:00.225]
Iter=31 [00:00:04.663] [-]loss: 22254.9070 [00:00:00.227]
Iter=32 [00:00:04.623] [-]loss: 22076.8149 [00:00:00.229]
Iter=33 [00:00:04.663] [-]loss: 21908.3398 [00:00:00.225]
Iter=34 [00:00:04.580] [-]loss: 21748.6950 [00:00:00.224]
Iter=35 [00:00:04.528] [-]loss: 21597.1818 [00:00:00.227]
Iter=36 [00:00:04.535] [-]loss: 21453.1786 [00:00:00.224]
Iter=37 [00:00:04.541] [-]loss: 21316.1297 [00:00:00.223]
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Iter=39 [00:00:04.525] [-]loss: 21060.9505 [00:00:00.227]
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Iter=41 [00:00:05.074] [-]loss: 20828.2120 [00:00:00.334]
Iter=42 [00:00:04.910] [-]loss: 20719.3558 [00:00:00.237]
Iter=43 [00:00:04.868] [-]loss: 20615.0902 [00:00:00.225]
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Iter=45 [00:00:04.791] [-]loss: 20419.2337 [00:00:00.238]
Iter=46 [00:00:04.867] [-]loss: 20327.1489 [00:00:00.417]
Iter=47 [00:00:04.936] [-]loss: 20238.6622 [00:00:00.239]
Iter=48 [00:00:04.645] [-]loss: 20153.5715 [00:00:00.225]
Iter=49 [00:00:04.534] [-]loss: 20071.6890 [00:00:00.222]
Iter=50 [00:00:04.689] [-]loss: 19992.8403 [00:00:00.250]
Iter=51 [00:00:04.882] [-]loss: 19916.8628 [00:00:00.322]
Iter=52 [00:00:04.776] [-]loss: 19843.6050 [00:00:00.244]
Iter=53 [00:00:04.818] [-]loss: 19772.9253 [00:00:00.240]
Iter=54 [00:00:04.820] [-]loss: 19704.6913 [00:00:00.225]
Iter=55 [00:00:04.790] [-]loss: 19638.7789 [00:00:00.227]
Iter=56 [00:00:04.571] [-]loss: 19575.0719 [00:00:00.253]
Iter=57 [00:00:04.769] [-]loss: 19513.4613 [00:00:00.314]
Iter=58 [00:00:04.753] [-]loss: 19453.8449 [00:00:00.238]
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Iter=64 [00:00:04.760] [-]loss: 19133.0329 [00:00:00.234]
Iter=65 [00:00:04.780] [-]loss: 19084.9873 [00:00:00.227]
Iter=66 [00:00:04.754] [-]loss: 19038.3105 [00:00:00.235]
Iter=67 [00:00:04.734] [-]loss: 18992.9438 [00:00:00.276]
Iter=68 [00:00:04.690] [-]loss: 18948.8313 [00:00:00.243]
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Iter=71 [00:00:04.743] [-]loss: 18823.5089 [00:00:00.258]
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Iter=76 [00:00:04.913] [-]loss: 18635.3447 [00:00:00.331]
Iter=77 [00:00:05.078] [-]loss: 18600.4658 [00:00:00.241]
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Iter=79 [00:00:04.822] [-]loss: 18533.1906 [00:00:00.236]
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Iter=86 [00:00:05.050] [-]loss: 18320.8408 [00:00:00.308]
Iter=87 [00:00:04.885] [-]loss: 18293.1130 [00:00:00.232]
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Iter=89 [00:00:04.664] [-]loss: 18239.4026 [00:00:00.247]
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Iter=99 [00:00:04.574] [-]loss: 18001.3682 [00:00:00.225]
Iter=100 [00:00:04.542] [-]loss: 17980.0124 [00:00:00.225]
Iter=101 [00:00:04.664] [-]loss: 17959.0506 [00:00:00.270]
Iter=102 [00:00:04.820] [-]loss: 17938.4716 [00:00:00.226]
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Iter=116 [00:00:04.527] [-]loss: 17685.2838 [00:00:00.225]
Iter=117 [00:00:04.621] [-]loss: 17669.3745 [00:00:00.228]
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Iter=119 [00:00:04.529] [-]loss: 17638.3119 [00:00:00.228]
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Iter=493 [00:00:04.580] [-]loss: 16292.7369 [00:00:00.223]
Iter=494 [00:00:04.572] [-]loss: 16292.0329 [00:00:00.225]
Iter=495 [00:00:04.734] [-]loss: 16291.3323 [00:00:00.240]
Iter=496 [00:00:04.640] [-]loss: 16290.6351 [00:00:00.229]
Iter=497 [00:00:04.583] [-]loss: 16289.9411 [00:00:00.229]
Iter=498 [00:00:04.594] [-]loss: 16289.2505 [00:00:00.229]
Iter=499 [00:00:04.606] [-]loss: 16288.5632 [00:00:00.228]
MF_extALS <hr, ndcg, prec>: 0.1931 0.0471 0.0161 [00:40:58.699]