Neighbor interaction-based personalised transfer for cross-domain recommendation

Figures & data

Figure 1. A simple illustration of model comparison. (a) Existing mapping-based CDR methods learn personalised mapping functions only considering the user's interactions. (b) The proposed NIPT-CDR considers the user's interactions, together with neighbour users' interactions when learning personalised mapping functions.

Table 1. Summary of notations.

Notations	Descriptions
${\mathcal{U}}^s,{\mathcal{U}}^t$	set of users in the source (target) domain
${\mathcal{V}}^s,{\mathcal{V}}^t$	set of items in the source (target) domain
$\mathcal{O}$	set of overlapping users
${\mathcal{R}}^s,{\mathcal{R}}^t$	interaction matrix from the source (target) domain
$U^s,U^t$	user embedding matrix of the source (target) domain
$I^s,I^t$	item embedding matrix of the source (target) domain
$H_{u_i}^s$	list of interaction items for user $u_i$ in the source domain
n	number of interaction items
k	number of similar users
m	maximum number of interaction items

Figure 2. An illustrative figure of the NIPT-CDR framework.

Figure 3. Intra-domain item supplementing module process.

Figure 4. Attention weight calculation process.

Table 2. Statistics of different cross-domain scenarios on the Amazon dataset.

	Dataset		Item		User			Inter
Scenario	Source	Target	Source	Target	Source	Target	Overlap	Source	Target
Scenario1	Movie	Music	50,052	64,443	123,960	75,258	18,031	1,697,533	1,097,592
Scenario2	Book	Movie	367,982	50,052	603,668	123,960	37,388	8,898,041	1,697,533
Scenario3	Book	Music	367,982	64,443	603,668	75,258	16,738	8,898,041	1,097,592

Table 3. The comparison of the baselines and our method.

Model	Training Data	Transfer Strategy	Technology Adoption or Basic Idea
CMF	Rating	Shared embedding	Multiple relations
DCDCSR	Rating	Common mapping	DNN mapping
SSCDR	Rating	Common mapping	Semi-supervised learning
EMCDR	Rating	Common mapping	MLP-based nonlinear mapping
CATN	Rating, content	Common mapping	Aspect correlation learning
PTUPCDR	Rating	Personalized mapping based on user interaction	Meta-network
NIPT-CDR	Rating	Personalized mapping based on user and neighbour interactions	Attention mechanism, meta-network, nearest neighbour search algorithm

Table 4. Comparison of the model NIPT-CDR with other models in terms of the MAE.

	Scenario 1			Scenario 2			Scenario 3
Scenario	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$
TGT	4.4997	4.5127	4.5232	4.2195	4.2493	4.2456	4.5192	4.0591	4.5262
CMF	1.5399	1.7236	2.6086	1.4508	1.6032	2.1636	1.8624	2.1748	3.1326
DCDCSR	1.5012	1.8447	2.7991	1.4124	1.6895	2.3698	1.8718	2.2446	3.1903
SSCDR	1.3828	1.6562	1.9829	1.3061	1.2454	1.3291	1.5903	1.6891	2.1144
EMCDR	1.3641	1.6059	2.0857	1.1609	1.1876	1.3310	1.4812	1.7641	2.3084
CATN	1.2671	1.4890	1.8182	1.1249	1.1598	1.2672	1.3924	1.6023	1.9571
PTUPCDR	1.1828	1.2857	1.6057	1.0787	1.1141	1.2182	1.2330	1.3694	1.7843
NIPT-CDR	1.1098${}^{\ast }$	1.2398	1.5830${}^{\ast }$	1.0672${}^{\ast }$	1.1028${}^{\ast }$	1.2004	1.2281${}^{\ast }$	1.3568${}^{\ast }$	1.7673${}^{\ast }$

Notes: ${}^{\ast }$ denotes significance level at 0.05, paired t-test for best baseline and NIPT-CDR.

Table 5. Comparison of the model NIPT-CDR with other models in terms of the RMSE.

	Scenario 1			Scenario 2			Scenario 3
Scenario	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$	$20\mathrm{\%}$	$50\mathrm{\%}$	$80\mathrm{\%}$
TGT	5.1814	5.1943	5.2018	4.7840	4.8592	4.8397	5.1855	5.1810	5.2055
CMF	2.1862	2.3561	3.3318	1.8447	2.1002	2.6941	2.4517	2.8411	3.9791
DCDCSR	2.0156	2.4497	3.6215	1.8137	2.0632	2.7786	2.3451	2.8908	3.8820
SSCDR	1.7847	2.0923	2.3108	1.6970	1.5844	1.7154	1.9736	2.2217	2.7003
EMCDR	1.6691	2.0155	2.4925	1.4742	1.5058	1.6454	1.9150	2.1223	2.6526
CATN	1.6468	1.9205	2.2991	1.4548	1.4826	1.6280	1.7399	2.0665	2.5623
PTUPCDR	1.5519	1.7443	2.2351	1.3945	1.4507	1.6215	1.6421	1.8548	2.4022
NIPT-CDR	1.4469${}^{\ast }$	1.6861	2.1801${}^{\ast }$	1.3785	1.4365${}^{\ast }$	1.6061	1.6320	1.8349${}^{\ast }$	2.3884${}^{\ast }$

Notes: ${}^{\ast }$ denotes significance level at 0.05, paired t-test for best baseline and NIPT-CDR.

Figure 5. Based on the MF model, the NIPT-CDR compares with EMCDR and PTUPCDR. In (a), we employ MAE metric to evaluate the model's performance, and in (b), the metric is RMSE.

Figure 6. Based on the GMF model, the NIPT-CDR compares with EMCDR and PTUPCDR. In (a), we employ MAE metric to evaluate the model's performance, and in (b), the metric is RMSE.

Figure 7. The influence of changing the total number m of interaction items on different cross-domain scenarios in NIPT-CDR. (a) the change curve of the MAE metric and (b) the change curve of the RMSE metric.

Table 6. The comparison results of ablations studies on three cross-domain scenarios.

	Scenario 1		Scenario 2		Scenario 3
Scenario	MAE	RMSE	MAE	RMSE	MAE	RMSE
NIPT-CDR	1.0863	1.4161	1.0775	1.3826	1.2548	1.6597
NIPT-CDR${}_{IS}$	1.1435	1.4916	1.0784	1.3831	1.2686	1.6710
NIPT-CDR${}_{ATT}$	1.1335	1.4864	1.1021	1.4031	1.2562	1.6641

Figure 8. F1-score experimental results graph.

Table 7. Statistics of different cross-domain scenarios on the Movielens-25M dataset.

Dataset		Item		User			Inter
Source	Target	Source	Target	Source	Target	Overlap	Source	Target
Adventure	Crime	3186	4483	80,829	80,242	39,935	2,775,635	1,969,286
Action	Thriller	4097	5345	80,006	79,596	39,346	2,084,853	1,742,554

Figure 9. Comparison results on the Movielens-25M dataset. In (a), we employ the MAE metric to evaluate the model's performance, and in (b), the metric is RMSE.