Hybrid Recommender System based on Deep Learning Model

doi:10.23940/ijpe.20.01.p13.118129

Abstract

Abstract:

Mashups, which can produce enriched results using fast integrating application programming interfaces (API) and data sources, play a pivot role in building web-based and mobile applications. However, the rapidly increasing number of APIs makes it difficult to choose suitable APIs for a mashup, especially when the historical relationships between APIs and mashups are very sparse. Many researchers have sought to develop algorithms to recommend APIs to mashups. Some hybrid approaches integrate model-based collaborative filtering and auxiliary information and have achieved good prediction performance. However, there are few models that analyze the contextual information of services' descriptions. In addition, many approaches explore the context information of APIs, but few of them consider the context information of mashups. This paper presents a new model named CDHMF for recommender systems, which uses the Word2Vec technique to integrate the contextual information into a probabilistic factorization matrix (PMF). We analyze descriptions not only for APIs but also for mashups. Our experiments are performed with datasets from ProgrammableWeb. The results show that CDHMF significantly outperforms some state-of-the-art recommender systems in mashup service applications.

Key words: service recommending, deep learning, PMF, CNN, mashup, API

Chang Su and Deling Huang. Hybrid Recommender System based on Deep Learning Model [J]. Int J Performability Eng, 2020, 16(1): 118-129.

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Figures/Tables 32

E = - 12 ∑ i N ∑ j M I ij (R i j - U i T V j) 2 - λ u 2 ∑ i N U i T U i - λ v 2 ∑ j M V j T V j

(1)

Figure 1.

p (R | U, V, σ 2) = ∏ i N ∏ j M N (r ij | u i T v j, σ 2) I ij

(2)

p (U | Wm, Dm, σ U 2) = ∏ i N N (u i | CNN (D m i), σ U 2) I

(3)

p (V | Wa, Da, σ V 2) = ∏ j M N (v j | CNN (D a j), σ V 2) I

(4)

p U, V, Wm, Wa R, Dm, Da, σ 2, σ U 2, σ V 2, σ Wm 2, σ Wa 2 ≈ p R U, V, σ 2 p U Wm, Dm, σ U 2 p Wm σ Wm 2 p V Wa, Da, σ V 2 p Wa σ Wa 2

(5)

Figure 2.

c = f W c × D + b

(6)

c = max c 1, c 2, ..., c l - ws + 1

(7)

d = c 1, c 2, ..., c n c

(8)

s = tanh W 2 tanh W 1 d + b 1 + b 2

(9)

max ? U, V, W p (U, V, Wm, Wa | R, Dm, Da, σ 2, σ U 2, σ V 2, σ Wm 2, σ Wa 2) = ma x U, V, W p (R | U, V, σ 2) p (U | Wm, Dm, σ U 2) p (Wm | σ Wm 2) p (V | Wa, Da, σ V 2) p (Wa | σ Wa 2)

(10)

L = ∑ i N ∑ j M I ij 2 (r i j - u i T v j) 2 + λ u 2 ∑ i N | | u i - CNN (D i) | | 2 + λ v 2 ∑ j M | | v j - CNN (D A) | | 2 + λ w ∑ k w k | | w k | | 2

(11)

u i = (V I i V T + λ U I K) - 1 V R i + λ u CNN D Mi

(12)

u i = (U I j U T + λ V I K) - 1 V R j + λ v CNN D Aj

(13)

L (W M) = λ u 2 ∑ i N | u i - CNN D A, T | 2 + λ w 2 ∑ k w k | w k | 2

(14)

? w k L (W M) = - λ U ∑ i N (u i - ? w k CNN D M i + λ W w k

(15)

L (W A) = λ v 2 ∑ j M | | v j - CNN (D A, T) | | 2 + λ w 2 ∑ k w k | | w k | | 2

(16)

? w k L (W A) = - λ V ∑ j M (v j - ? w k CNN (D A j, T j)) + λ W w k

(17)

Figure 3.

Table 1

RMSE = ∑ i, j N, M (r ij - r ? ij) 2 number ? of ? ratings

(18)

Figure 4.

Figure 5.

Recall = R A i ∩ RM A i RM A i

(19)

Recall = R (A i) ∩ RM (A i) RM (A i)

(20)

precision = 2 × recall × precision recall + precision

(21)

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

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Model	Ratings	Documents	Context-info	Tags
PMF	Yes	No	No	No
CDL	Yes	Yes	No	No
R-ConMF	Yes	Yes	Yes	No
CFSMF	Yes	Yes	Yes	Yes

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