Untangle: Critiquing Disentangled Recommendations
Authors: Preksha Nema
Alexandros Karatzoglou
Filip Radlinski
Venue:  (2021)
Keywords: Disentangling
Recommender Systems
VAE
Critiquing
Explainability

Abstract: The core principle behind most collaborative filtering methods is to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus difficult for users to control their recommendations based on particular aspects (critiquing). In this work, we propose Untangle: a recommendation model that gives users control over the recommendation list with respect to specific item attributes, (e.g.:less violent, funnier movies) that have a causal relationship in user preferences. Untangle uses a refined training procedure by training (i) a (partially) supervised β-VAE that disentangles the item representations and (ii) a second phase which optimized to generate recommendations for users. Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy. Moreover only a tiny fraction of labeled items is needed to create disentangled preference representations over attributes. 



Under review as a conference paper at ICLR 2021
Untangle: CRITIQUING DISENTANGLED RECOMMENDATIONS
A Anonymous authors
Paper under double-blind review
ABSTRACT
The core principle behind most collaborative filtering methods to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus difficult for users to control their recommendations based particular aspects (critiquing). In this work, we propose Untangle: a recommendation model that gives users control over the recommendation list with respect to specific item attributes, (e.g.leess violent, funnier movies) that have a causal relationship in user preferences. Untangle uses a refined training procedure by training (i) (partially) supervised 3- A-V that disentangles the item representations and (ii) second phase which optimized to generate recommendations for users. Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy. Moreover only tiny fraction of labeled items is needed to create disentangled preference representations over attributes.
INTRODUCTION
User and item representations form the basis of typical collaborative filtering recommenda- tion models. These representations can be learned through various techniques such as Matrix Factorization (1; or are constructed dynamically during inference e.g. the hidden state of RNN's in session-based recommenda- tions (3; 4).
Decoder
Disentangle
Recommend
As most standard recommendation models solely aim at increasing the performance of the system, no special care is taken to sure interpretability of the user and item rep- resentations. These representations do not explicitly encode user preferences over item attributes. Hence, they cannot be easily used by users to change a.k.a. critique (5) the recommendations. For instance, a user in recipe recommendation system cannot ask for recommendations for a set of less spicy recipes, the spiciness is not explicitly encoded in the latent space. Moreover the explainability of the recommendations that are provided by such systems is very limited.
p(h
(a)
Humey
:
0--00
P(1N
ax)
Encoder
Untangle model is trained in two phases: Disentangling phase: Input to en- coder is a one hot representation of an item (green dotted line). Obtained representation disentangled across attributes. Recom- mendation phase: Input to encoder is the items user interacted with (solid red line) and recommends new items.
Figure 1:
In this work, enrich state-of-the-art rec- ommendation model to explicitly encode pref- erences over item attributes in the user latent space while simultaneously optimizing for rec- ommendation's performance. Our work is

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motivated by disentangled representations in other domains, e.g., manipulating generative models of images with specific characteristics (6) or text with certain attributes (7). Varia- tional Autoencoders (VAEs), particularly B-VAE's (8) (which adapt here), are generally used to learn these disentangled representations. Intuitively, they optimize embeddings to capture meaningful aspects of users and items independently. Consequently, such embeddings will be more usable for critiquing.
There are two types of disentangling 3-VAEs: unsupercised and supervised. In the former, the representations are disentangled to explanatory factors of variation in an unsupervised manner, i.e. without assuming additional information on the existence (or not) of specific aspects. Used in the original 3- V-VAE (8) approach, lack of supervision often results in inconsistency and instability in disentangled representations (9). In contrast, in supervised disentangling, small subset of data is assumed to have side-information (i.e. label or a tag). This small subset is then used to disentangle into meaningful factors (10; 9). As critiquing requires user control using familiar terms/ perms/attiln attributes, incorporate supervised disentanglement in a 3- VAE architecture in this work.
To achieve the explicit encoding of preferences over item altributes in embedding space we refine the training strategy of the untangle model. We essentially train in two phases: i) Disentangling phase: We explicitly disentangle item representations, using very few supervised labels. ii) Recommendation phase: We encode the user, using the bag-of-words representation of the items interacted, and then generate the list of recommended items. Untangle gives fine-grained control over the recommendations across various item attributes, as compared to the baseline. We achieve this with a tiny fraction of attribute labels over items, and moreover achieve comparable recommendation performance compared to state-of-the-art baselines.
2 RELATED WORK
Deep learning based Autoencoder architectures are routinely used in collaborative filtering and recommendation models (11; 12; 13). In particular (11; 12) adopt denoising autoencoder architectures, whereas (13) uses variational autocncoders. The internal (hidden) representa- tions generated by the encoders these models are not interpretable and hence cannot be used for critiquing explanations in recommendatiioss
Recent work on Variational Autoencoders across domains have focused on the task of generating disentangled representations. One of the first approaches used to that end was 3-VAE (8; 14; 15), which essentially enforced a stronger (multiplying that term with 8 > 1) KL divergence constraint on the VAE objective. Such representations are more controllable and interpretable as compared to VAEs.
One of the drawbacks of 8- 8-VA is that the disentanglement of the factors cannot be controlled and that they are relatively unstable and not easy to reproduce particularly when the factors of variance subtle (9; 14; 16; 17). This has motivated methods that explicitly supervise the disentangling (10), that rely either on selecting a good set of disentangling using multiple runs and the label information (18), or by adding a supervised loss function in the 3- 8-V objective function (10). As supervised disentangling methods are better in explainability and could provide control over desired attributes, we motivate our model from (19) for better critiquing in VAE based recommendation systems.
In recommender systems similar methods to utilize side information, have also been used recently to allow for models that enable critiquing of recommendations. These models allow users to tune the recommendations across some provided attributes attributes/dimmesions dimensions. Notable examples are (20; 21), where the models are augmented with classifier of the features over which to control the recommendation. Adjusting the features at the output of the classifier modifies the internal hidden state of the model and leads to recommendations that exhibit or not the requested attribute. Note that this method of critiquing is quite different to our approach which allows for a gradual adjustment of the attributtes. Moreover the models in (20; 21) require a fully labeled dataset with respect to the attributes while our approach only requires small fraction of labeled data.
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Unsupervised cisentanglement also recently used to identify and potentially use factors of variation from purely collaborative data i.., data generated by user interactions with items (22) note though that this method focus was mainly on performance of the recommen- dations and that does not allow for seamless critiquing as it is not clear what aspect of the data get disentangled.
3 Untangle
The aim of the unlangle model is to obtaining controllable user (and item) representation for better critiquing along with optimizing for recommendation performance. To this end, we incorporate simple supervised disentanglement technique to disentangle across item attributes/ characteristics over which we want to provide explicit control to the users.
We index users with u E {l...., n), and items with E (l....,m.. X is a matrix of user-item interactions (Ziu = 1 if user interacted with item i, and otherwise). A subset of items are assumed to have binary labels for attributes A.
Our model is modified 3-VAE architecture, with feed forward network based encoder and decoder. In Figure 1, user u is represented by [z : c]. Note that stands for concatenation, the 2 part of the representation is non-interpretable by default while the c part of the representation we map (through a refined learning step) the representation of the attributes of the items over which we would like the user to have control. Each dimension in с is mapped to only one attribute a. Across the paper, we refer the dimension associated with the attribute a, as Ca. The user representation is sampled from the distribution paramcterized by the encoder (90): (z = .N (Pg(TTu)) diag(o The imput to the encoder is the bag of words representation of the items u interacted with, iee. the th row of matrix X, Tux. The decoder generates the probability distribution given user representation [z c) (=) x exp(fge(((z cl)), over the m items. The likelihood function used in recommender system settings (3; 23; 24; 25) is lypically the multinomial likelihood:
po(zulle [[ c))
Σ Tui log (1 : c)))
3.1 LEARNING
Training is conducted in two phases: in Algorithm 1.
Recommendation and Disentangle phase, mentioned
Recommendation Phase The objective in this phase is to optimize the encoder parame- terized by (0), and decoder parameterized by (1b) to generate personalized recommendations. We train our model with the following objective:
[( 0, ()
(oo [z : c]]] - 3K L(g =] p(1z c)))
(1)
Intuitively, this is the negative reconstruction error minus the Kullback-Leibler divergence enforcing the posterior distribution of to be close to the Gaussian distribution (prior) p(z).
The KL divergence VAE is computed between the representation sampled from the encoder and the normal distribution p(z) Ia). The diagonal co-variance matrix enforces a degree of independence among the individual factors of the representation. Con- sequently, increasing the weight of the KT. divergence term with independence criteria, leading to disentangled representation. This ensures that even in the recommendation phase, the learnt user representations are nudged towards disentanglement.
boosts the feature
Disentanglement Phase Since the attribute information is commouly available across the items. In this phase, we first obtain the item representation in the user latent space (as depicted in the highlighted green box in Figure 1). We pass the one hot encoding of an item, and obtain its representation in the latent user space. We then disentangle the obtained representation using the following objective:
(lo L(1, = ((sce])) [logpo (1, [2: c])]
(2)
3K BKT(go (z (]] c))) + (dt.
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Algorithm 1: Untangle: Training
Data: X E Raxm containing user-item interactions, with a subset of items having labels
for A attributes
initialize model params.: Encoder(o), Decoder(O)
2 do
if is disentangle then
// Disentangle representations
1, < random mini batch from set of items that are labelled with A set.
[ f sample from N (I:)) diag
Tia- <- Decoder((2 c])
compute gradients Lo Vo using Objective 2
4- + VL Li
0 - 0 Lo end
10
if is recommend then
// Recommend items
Xux t- random mini-batch from dataset
2 ::c] f- sample from N (Ho(Taa) diag( as (x..)
18
Su. Decoder([z cl)
compute gradients V Lg using Objective 1
15
16
o+ Lg
17
0 + 0+ La
18
end
19
while model converges;
As in (10), modify the 3-VAE objective (Objective 1) in to incorporate classification loss over the factors c, over which we disentangle. This loss penalizes discrepancies between the attribute label prediction for factor e, and the label of interest, nudging the disentanglement for each attribute to happen over the corresponding factor Ca-
4 DATASETS
Movielens Dataset: We use the Movielens- Im and Movielens- 20m datasets (26), which contain 1 million and 20 million user-movie interactions, respectively. For the latter, we filter out movies with fewer than 5 ratings and users who rated < 10 movics. We utilize the relevance scores given in the Moviclens datasct for 10,381 movics across 1,000 different tags to select attributes for disentangling. E.g,, Mission Impossible movie has high relevance (0.79) for the action tag. We take the lop 100 lags, based on the mean relevance score across all movies. Among these 100 tags, some tag pairs, like (funny, and very funny), are by definition entangled. Therefore, to identify distinct tags, we cluster these 100 tags (E R 10381 movies) into 20 clusters using K-Means clustering. Finally, select subset from these 20 clusters, as given in Table for disentangling. We assign the new-clustered tag (as given in Table Column 1) if the average-relevance score (the mean of relevance scores for tags present in the corresponding cluster) is higher than 0.5.
Goodreads Dataset: The GoodReads dalaset (27) contains user-book interactions for different genres. We use the Children and Comics genres to evaluate our model. We filter out items rated < 5 and users who rated < 10 books. The final statistics are given in Appendix A. We extract the tags for disentangling from the user- generated shelf names, e.g., hustorical-fiction to-read. We retrieve the top 100 shelf names. Some tags (like "books-i-have)) are not useful to revise recommendations. Therefore, we only consider item attributes that all the authors consider informative for critiquing recommendations We select a subset for disentangling from this set, it still contains correlated attributes like historical-fictiin fiction. We select attributes with the corresponding number of books where the attribute was present 1ho0 humor:9318, mystery:3589, and romance:1399) and fadventure:8162. horror:5518, numor:8314, mystery:5194, romance:7508, sci-fi:792)), for Goodreads- (Children and Comics) respectively.
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Tagged movies
Cluster Label
Tags included in cluster
action
1,167
action, fight-scenes, special-effects comedy, funny, goofy, very funny
funny
1,219
romantic
975
destiny, leel-good, love story, romantic
sad
1,488
bleak, intimate, loneliness, melancholic, reflective, sad betrayal, murder, secrets, suspense, tense, twist-and-turns brutality, cult classic, vengeance, violence, violent.
suspense 1,070
violence 1,297
Table 1: Each cluster was manually assigned a human-readable label. Some of the tags present each cluster are listed in column 3. Column lists the number of movies that had high relevance score for tags in each cluster.
EVALUATION METRICS
We evaluate Untangle on these criteria: disentanglement, iii) control/critiquus based on the disentangled representations.
quality of items recommended, ii) extent of
Ranking Based Metrics: We evaluate the quality of items recommended using two ranking- based metries: Recall@k and normalized discounted cumulative gain NDCG@k. The latter is rank sensitive, whereas Recall @k considers each relevant item in the top-k equally.
Recall@k Em [ittem] E S] min(k, [S])
2 -
DCG Σ
log(i + 1)
NDCG is normalized DCG by dividing it by the largest possible DCG@k.
Disentanglement Metrics: We the Disenlanglement, and Completeness metrics introduced in (28). Disenlanglement measures the extent to which each dimension captures at most attribute. F.g., if dimension captures all attributes, the Disentanglement score will be O. We compute importance Paj of th attribute on jth dimension of [z c] e Rd, with Gradient Boosted Trees as given in (9). Using the Paj scores, the disentanglement score is defined as:
|A|-1
H[)) = Σ Poslog|AlPass
D2 = (1- (1=(1HAA(P)))
Σ
|A| -1
D EpDD,
2a=0 Paj
Pj
L- |A
|A
21=0 2a=0
Poj
We compute entropy H (P)) for jth dimension. Disentanglement score for dimension j is then entropy. The final disentanglement score of the system is weighted average of D; across all the dimensions d, where Pj the dimension's relative importance. Completeness: attribute is encoded in single dimension of [z c). For
Measures the extent to which a latent representation of 16 dimensions and 2 attributes, if 8 dimensions encode attribute aj and the other encode 0g, then the Disentanglement will be but Completeness will be 0.25. Completeness is defined as:
d-1
Ha(Pa) = Σ
PaplogaPaj (1-
1=0
AA-1
C
Σ a=0
Ca.a
Pa
=== Paj
Σ E5 Paj
Controller Metric: We propose a simple metric to quantify the extent control disen- tangled dimension c, has on recommendations by critiquing attribute With supervised- disentanglement, the mapping between dimensions с in the latent representations, and the attributes across which we disentangled is known. The features in these dimensions in allow the user to control/ control/critiqus /rittque the respective attribute in the generated recommendations. For instance, less violence can be achieved by reducing the corresponding dimension value
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Recommendation Performance
Disentanglement Performance
Dataset
Model
N
R3600
R:350
Disent.
Comp.
Controller Metric
MuKi-DAE
0.38782
0.31636
0.43404
0.317
0.214
0.961
VAE
0.39252
0.32515
0.44757
0.306
0.200
0.947
ML-1m
B-VAE
0.38658
0.31216
0.43032
0.313
0.0211
0.924
Untangle
0.37833
0.30079
0.42532
0.543
0.393
T9.27
Multi-DAE
0.39738
0.37071
0.50847
0.265
0.182
0.88
VAE
0.39827
0.37212
0.50946
0.246
0.167
3.53
ML-20m
B-VAE
0.38724
0.35617
0.48976
0.211
0.142
3.27
Untangle
0,40320
0.37367
0.51303
0.677
0.529
75.11
Multi-DAA
0.42593
024022
U.52610
0.243
0.175
0.963
Multi- VAE
0.45159
0.45697
0.55598
0.173
0.137
0.872
GR-Comics
B-VAE
0,44366
0.44949
0.65226
0.192
0.146
0.847
Untangle
0.43597
0.43981
0,54218
0.733
0.536
73.41
Multi- DAE
0.40030
0.43240
0.56473
0.145
0.132
2.37
Multi- VAE
0.40219
0.43057
0.56695
0.164
0.132
0.86
GR-Children
B-VAE
0.40219
03005
0.56695
0.139
0.103
0.92
Untangle
0.41255
0.44490
0.58473
0.517
0.374
14.37
Table Recommendation and Disentanglement performance on Movielens- (atellenss(mm22000 21m,20m) and Goodreads- Coalleadds-Comiice Childre))) domain dataset on the corresponding test split.
(violence) in We evaluate this by probing if the items where the attribute is present (Sa) are ranked higher when the dimension value ca is increased by a factor of in the user representation. We extract the items recommended from the decoder (Ia (g)), for the new user representation where only is multiplied X Ca. We compare (g)) against (Sa) using any ranking-based metric described above. We further vary g for a given range [-G,G], and study if the ranking of (Sa) improves. The Controller-Metric is defined as follows:
Controller Batller__Meiii(k;; g) [Recall@((Ia(G)) S.) Recall@k((a(GG)) Recal(@H(LL-GG)...)
(3)
To compute the Controllen-Metric for a system, we take the median across all the attributes disentangled in c. Note that the metric value depends k and the range chosen.
6 RESULTS AND DISCUSSIONS
Recommendation and Disentanglement Performance We train the Untangle model with the parameter settings mentioned in Appendix B. We compare Untangle with the MultiDAE, and MultiVAE models (13). We also compare our model with stronger bascling for disentanglement 8-VAE, which discntangles the representation in an unsupervised way. We present our results in Table 2. Note that supervised disentanglement for Table 2, has been trained with 300 (1%), 1030 (5%), 1500 (5%), 1550 (5%) labelled items for Movielens- (1m,20m) and Goodreads- Sitrraad-sChiddrenn (Children,Comiln Comics) respectively. We observe that our proposed model's performance on ranking- based metrics (Recall@k, and NDCG@k) is comparable to the baselines across all datasets. Thus we show that disentangling the latent representation does not impact the recommendation performance. We also quantify the disentanglement using the Disentanglement and Completeness metrics discussed in Section 5. We infer from Table 2 that the disentanglement achieved across all the mentioned strategies is significantly higher than the baselines. Disentangling with a tiny fraction of labeled items leads to significant gain in disentanglement compared to 8-VAE.
We evaluate the extent of the controllability of the disentangled representations. To this end, we compute the Controller Metric, which measures the control over the attribute dimension Ca variation. We use the multiplicative range [-150, +500 to amplify ca, and measure the ranking performance using recall@10 across this range. Note that the rest of the representation remains unchanged. We observe that we get significantly higher controllability for the Untangle model compared to the baseline approaches, especially for Movielens and Goodreads-Comice dataset. By reducing Ca we can diminish the existence of items with attribute from the recommendation list and by gradually increasing the magnitude of c, increase the presence of items with this attribute in the recommendation list up to saturation.

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Albribute
1.0
1.0
0.8
200.8
Panking Mer-c
0.8
Dantiig Metree
0.6
0.6
0.6
0.6
0.4
0.4
0.4
00
-100-100 -50 2 50
100 150
BB
1150-100
50 0
50
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1500-100
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Atrihde-attaiio musipiiation faching
mubipication farling
muttlicatis farling
muttppinat
(a) MultiVAE:Sad
(b) MultiVAERRomnttic (c) MultiVAE.Suspense
(d) MultiVAE:Violnnce
1.0
Attrbute be
1.0
Actribude romentir
Aftritadte
Attriule stolence
Raninn Mentts
Deniong Mentic
van M833
Bercora Mertis
New 1830
Nor 300
0.0
0.0
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1500-100
100 150
6.0
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130
-30 0
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50
variable multpication
Atributcc mutiilicaiinn
Attrbuus muttblication factong
factong
(e) Untangle:Sad
(I) Untangle:Romantic
(g) Untangle:Suspense
(h) Untangle:Violenee Violence
Figure 2: Control over recommendations when Pactor-value ca: is adjusted by multiplicative
factor g E [-150, 150]. Recommendation lists are evaluated by 15,121@(),10.20), Relevance is determined by the presence of altribute in the retrieved items. We compare Multi-VAB (lop) with Untangle model (bollom) for sad, romantic, suspense and violence on ML.20m.
1.0
Afti bu acvenhure
1.0
Aftribube wii-
1.U
Attritute mydtery
3.0
Altributte humer
023 NoI
Roniig wenes
U.U
0.U
Benkeng Mentiz
0.B
B.4
B.4
102
0.2
0.2
150-1100
50
0.0
-0
100
0.0
150 -30
150
wwrbat--sttsiiee muripication ferhing
Anr buleosttiie muttpicatiion ferting
ammibite-woriiee mumpieation farinng
Attibutt on ladde munpliaaton Seriiin
(a) MultiVAE:Adventen
(b) MultiVAE::ciiFFi
(c) MultiVAE.Mysterr
(d) MultiVAE:
Aftritate
Attritane mysbery
Attribett hurrer
modi-20
0.8
0.8
Banins Menten
0.8
Denong Mantin
Vec
0.6
0.6
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Roningg
Baniing
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1100-100- -50 5 50
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Aty bute varibble mutippication foctong
ADD bute vorizb e multpiicatin Tactong
ABd bute varlable multipication factorg
Attrbuut vo fablc mulipiication factong
(e) Untangle:Adventure
(f) Untangle:Sci-Fi
(g) Untangle:Mystery
(h) Untangle:Humor
Figure 3: We compare Multi- (top) with Untangle model (bottom) for adventure, sci-fi, mystery and humor attributes for Goodreads-Comics for the same analysis done in Figure 2.
Critiquing Recommendations The primary aim of our model is to obtain controllable representations for critiquing. With the Controller Metric, we quantify controllability, here we further analyze the incremental impact of changing the attribute dimension. In this analysis, we visualize the effect on the recommendations of the adjustment of the disentangled factor ca for each attribute a. We multiply the factor with g in Figure 2 and Figure 3 for baseline model MultiVAE and Untangle. Note that for the baseline (MultiVAE), we adjust the dimension that has the highest feature importance score computed using Gradient Boosting Classifier for attribute a.
For the movies domain (Figure 2), we observe that for MultiVAE (row 1) the variation in co has no clear correlation with the recommendation performance in terms of the presence or absence of items with this attribute. In contrast to MultiVAF, in the Untangle model, we consistently observe a significant and gradual variation across all the explicitly disentangled attributes A. Even for subtle attributes like suspense, we obtain a complete range of 1e from 0.0 to 1.0 We observe similar results for Goodreads comics dataset (Figure 3), where we again get gradual and significant change (approximately 1) across all the disentangled attributes.
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Correlation between Relevance Scores and ca We observe that disentangling across item representa- tions leads to a fine-grained control for critiquing. We further verify, if the achieved controllability is an outcome of high correlation between factor ca. and the truc relevance score across movies for attribute a for Movielens- 20m dalaset. We randomly sample 500 movies, and obtain their latent representation from the encoder. In Figure 4, we plot the obtained ca value to and the true relevance score for attribute action. We can infer from the Fig- ure 4 that the representations obtained from Untangle have a high Pearson correlation of 0.53 as compared to MultiVAE model (Pearson Corrclation: -0.03). The graphs for other attributes/ /tags are presented in Appendix C.
Correlation for action
Correlation for action
Altribute Label Volue
Attributte Label Value
66
04
-3.15-613--005 600 3.5 E3n a.15 6.0
-10
-0.5
00
05
1.0
1.5
Dimension Value
Dimension Value
Figure Correlation between learnt dimension value Ca to the true relevance score across 500 movies for Moviclens-20m
Fewer Labels for Disentangle- ment One of the advantages of Un- tangle is that it disentangles with very few labels. We train Untangle with fewer labeled items. Each point in in Figure 5 is average across 5 differ- ent runs with different random seeds. For Movielens labels yields a disentanglereet score of 0.51, which gradually increases up to 0.92 when trained with all labels. For Goodreads-Comice belled books we are able to achieve 0.52 disentanglement, which gradually increases to 0.93 when the model is trained with all the labels. Note that even with 1% labelled items, the disentanglement and completeness scores obtained are significantly higher than 3-VAE model:0.21 and 0.19 on Moviclens-20n and Goodreads-Come and respectively.
10
10
64
08
Meetic Sooree
26
Disentanglement
Disentangiement Completeness
Completerress
just. 1% attribute
16"
13"
Fraction of supervised dalasal used during
Fracton supervised calasel used during baining
Figure 5: Variation in Disentanglement and Com- pleteness metrics when model is trained with lesser labels for Movielens-20m and GooddeeeadssComcss.
with 1% la-
Controllable Attributes With the above analysis, we have established that Untangle leads to controllable representations. In this experiment, we identify if the controllability is restricted to the chosen set of attributes. Therefore, we apply Untangle to a larger set of tags for Movielens- 20m dataset. We cluster all the 1181 tags present in the dataset, using K-Means clustering into 50 clusters. The clustering strategy is similar to the one mentioned in Section 4. We then evaluate the controllability for each of the clustered-tage b. We explicitly encode the corresponding clustered-tag b using Untangle, using 5% of labelled items. The controller metric score is obtained for each tag, across 5 runs. In each run, we sub-sample four clustered tags out. of 40 to be disentangled along with the corresponding clustered tag b. This is done to model the impact disentangling a given attribute alongside with other attributes present in the dataset. We identify that across 40 clustered-tage we obtain controller-metric score of > 11.0 for over 21 tags. Some of the attributes which do not have a higher controller-metiii score includes:80s, crappy, philosophical, etc. These attributes are also unlikely to be critiqued by user. Some of the most controllable and least controllable tags have been listed in Appondix D.
CONCLUSION
Untangle archives the goals set, it provides control and critiquing over the user recommen- dations over a set of predefined item attributes. It does so without sacrificing recommendation quality and only needs small fraction of labeled items.
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Under review as a conference paper at ICLR 2021
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A DATASET STATISTICS
We have mentioned the number of interactions, users, and items for Movielens and Goodreads Dataset in Table 3.
Dataset
Number of In-
Number
of Number Items
of Sparsity Rate
teractions
Users
Moviclens- 1m
1,000,209
6,040
3,706
4.468 %
Movielens-2Onl
9,990,682
136, 677
20, 720
0.353 %
Goodreads-
3,371,518
92,993
33,635
0.108 %
Children
Goodreads- Comics
2,705,538
57,405
32,541
0.145 %
Table 3: Dataset statistics (after performing all filtering). The sparsity rate indicates the fraction of cells in the complete user-item matrix with known value.
B IMPLEMENTATION DETAILS
We divide the set of users into train, validation and test splits. Validation and test splits consist of 10% the users, across all datasets. For each user in the validation and test split, use only 80% of the items rated by them to learn the user representation. The remaining 20% used to evaluate the model's performance. This strategy similar to the one used by (13). For all the experiments, user's latent representation is restricted to 32 dimensions. The encoder and decoder consists of two layers with [600, 200] and [200, 600) hidden units respectively, each with ReLu activation. We conduct lyper-parameter tuning to identify and values from [5, 10, 50] and [5, 10, 50, 500] respectively. The threshold M to identify movics where the attribute present for Moviclens 0.5 and 0.4 respectively. All the models are run up to 50 epochs. We select the best model, based on its performance on validation dalaset for both NDCG@ 100 and Disentanglement score. We select less than 5% of items for supervised B- 3-VAE using stratified sampling.
and MovicLens- Im is taken as
C CORRELATION BETWEEN DIMENSION VALUE C. AND TRUE RELEVANCE SCORES ACROSS ITEMS
We compare the dimension value ca associated with an attribut a, to the true relevance scores present in the Movielens- 20mielens-22mn dataset. We show in Figure that across all the lags, the correlation is consistently higher for Untangle, when compared to MultiVAE.
D CONTROLLABLE ATTRIBUTES
Using Untangle, identify the clustered-tags, which are controllable for revising user recommendations. We have listed some of the most controllable and least controllable tags in Table 4. We also list the absolute recall difference obtained across each cluster.
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Recall Difference
Tags in the cluster:
10 Most Controllable Attributes
0.75933
action packed, adventure, big budget, cool, dynamic cgi action, exciting, fast paced, fighting, franchise, plot holes, series
0.75924
atmospheric, bleak, character study, downbeat, forceful, grim, master- piece, movielens top pick, powerful ending, tense, visceral corruption, intense, murder, police investigation, secrets, suspense, sus- penseful, thriller, twists turns beautiful scenery, betrayal, childhood, earnest, excellent, excellent script, exceptional acting, friendship, good acting, great movie, honest, idealism, justice, light, moral ambiguity, original plot, oscar, oscar winner, sacrifice, unlikely friendships, very good, witty
0.75461
0.75246
0.72529
classic, cult classic, gunfight, highly quotable, quotable comedy, funny, hilarious, humorous, very funny afi 100 (movic quotes), oscar (best actor), oscar (best cinematography), oscar (best picture), oscar (best supporting actor) adapted from:book, based on a book, based on book, books Tuture, futuristic, sci fi, sci-fi, science fiction, scili, special effects, technol- ogy gooly, silly, silly fun 10 Least Controllable Attributes erotic, sex, sexual, sexuality adolescence, bullying, coming of age, coming-ol-age, high school, school, teacher, teen, teen movie, teenager, teenagers, teens
0.72285
0.7144
0.70965
0.61973
0.59895
0.24986
0.24014
0.23056
anti-semitism, anti-war, best war films, bombs, civil war, fascism, geno- cide, german, germany, historical, history, holocaust, jewish, jews, mili- tary, nazi, nazis, poland, russian, war, war movie, wartime, world war i, world war ii, wwii
0.17843
broadway, dance, dancing, great music, hip hop, lyrical, music, music business, musical, musicians, rock and roll
0.17675
adapted from:comic, based on comic, based on comic, comic, comic book, comics, graphic novel, mutants, super hero, super-hero, superhero, superheroes, vigilante
0.1112
business, capitalism, controversial, documentary, factual, freedom, islam, journalism, oil, political, politics, propaganda, revolution, us history, world politics
0.08376
1970s, anti-hero, awesome soundtrack, california, crime, drugs, gangs, good music, great soundtrack, gritty, nostalgic, small town assassination, black comody, brainwashing, censorship, cynical, distopia, fighting the system, guilt, hotel, identity, intellectual, intelligent, ironic, manipulation, morality, off-beat comedy, oscar (best writing screenplay written directly for the screen), paranoid, philosophical, philosophy, surveillance, thought-provooing
0.06328
0.0432
mentor, original
0.00691
80s, awful, bad, bad acting, bad cgi, boring, camp, campy, cheesy, disaster, dumb, dumb but funny, horrible, idiotic, lame, mad scientist, nudity (topless), remake, ridiculous, stupid, stupid as hell, stupidity
Table Most controllable and least controllable tags obtained from Untangle
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Under review as a conference paper at ICLR 2021
Correlation
Correlation suspense
violence
Lave
-0.1
-C.65 occ 0.5 6:0
615
-8.2 -210-115-- 05 oc3 6:35 a10 ass
-2.5 -1.30-00000 -00d 6.0 ans 210 ous
-031 -1 E.00 100 oas ca5
Dimension
Dimensson
(a) MultiVAE:
(b) MultiVAE:Sad
(c) MulteVAEBSuspense
(d) KlltiVAE:Violenge
Cor relatioon for romantic
Correlation for sad
Correlation for suspense
Comreaation for viclence
80
AND
Dimension Value
Dimension Value
Dimension on Value
Dimension Value
(e) UomanticcRmmantc
(f) Untangle:Sad
(g) Untangle:Suspense
(h) Iutangle:Violenue
Figure 6: We compare Multi-VAF (top) with Untangle model (bottom) for the correlation between factor and true relevance scores.
13


Reviews:
[{"review_id": "yotvgl7SNXB", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The submission aims to develop a disentangled recommender that explicitly grants the users the ability to control a chosen aspect of the recommendation list. This is achieved by (1) learning disentangled user/item representations via beta-VAE and (2) aligning text tags with certain dimensions via an auxiliary loss (the term after gamma in Eq 2). Empirical results demonstrate that this supervised approach outperforms the unsupervised baselines in terms of disentanglement and controllability, e.g., it can explicitly control the recommender to recommend a list of romantic movies.\n\nStrengths:\n\nS1: Good motivation. Controllability is potentially valuable for recommender systems.\n\nS2: The proposed technique makes sense and is easy to implement.\n\nS3: The writing is clear and easy to follow.\n\nWeaknesses:\n\nW1: The abstract states that the submission is interested in how to recommend *less* violent movies, *funnier* movies, etc. However, the empirical results do not fully support this. In fact, what this submission achieves is how to recommend a movie that is violent or not violent (i.e., whether it is of the category action movies or not) (binary controllability). There is no evidence that the proposed technique can tell the difference between “less violent”, “violent”, “more violent”, “extremely violent” (continuous controllability). The proposed metric (Eq 3) cannot reflect the said continuous controllability, nor do the showcases in Table 4 support this.\n\nW2: The abstract states that “only a tiny fraction of labeled items are needed”. However, the provided empirical results (shown in Figure 5) are weak. First, the result about how the number of labels available affects the model’s controllability is missing in Figure 5, with only results on disentanglement and completeness being shown there. Second, 0.5+ (achieved via 1% labels) vs 0.9+ seems to be a large gap. It is unclear whether a model with scores of 0.5+ can produce reasonable recommendation lists or has any controllability. Third, there are already plenty of existing works on weakly-supervised disentanglement and are neglected and not compared by the submission.\n\nW3: It is not surprising at all that a supervised method can outperform unsupervised baselines by a large margin. Please make it clear what the challenges are in developing a supervised method for disentanglement.\n\nW4: There is no evidence that the demonstrated controllability is personalized. That is, would the algorithm recommends *different* romantic movies to two users, even if the two users have never seen any romantic movies before, when we ask the algorithm explicitly recommend a romantic movie. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "rrGBiv4NUZN", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary: \nThe paper proposes a framework to learn disentangled representations for collaborative filtering systems. To model the user-item interactions, the authors adopt the likelihood model proposed by \\beta-Multi-VAE. The auxiliary task of predicting item labels is considered to increase the disentanglement and the ability to control the recommendations. Practical performance and the properties of disentanglement are demonstrated in experiments on real data sets. \n\nPros: \n1. The paper focuses on a novel and important area for the recommender system. Learning disentangled representation might help obtain a model that is useful yet interpretable and controllable. \n2. Consistent and superior disentanglement performances on all datasets.\n3. The choice of experimentation metric is complete and the results are presented coherently.\n\nCons:\n\n1.  While being neglected by most literature. Using disentangled representation for recommendation is not entirely new. Therefore this paper needs a stronger baseline (e.g. [https://papers.nips.cc/paper/7174-learning-disentangled-representations-with-semi-supervised-deep-generative-models]) for disentanglement learning models besides the \\beta-Multi-VAE. \n\n2. The idea of utilizing external knowledge or labels in the recommendation is not new. And it’s not uncommon for such models to tolerate missing contextual attributes.  [https://dl.acm.org/doi/10.1145/3097983.3098094] How does the proposed model compare with these baselines?\n\n3. The selection of cut-off for ranking metrics is not very consistent. The superiority of the VAE models is sensitive to this choice as pointed in [https://dl.acm.org/doi/10.1145/3298689.3347058]\n\n4. The setting of controller metric experimentation seems to be trivial or unfair. The multiplier is applied to the dimension c_a, which is only defined for models trained with attribute labels. \n\n5. The presentation of the model is confusing. 1) What is 1_i in equation (2)? is it a bag of word representation of randomly sampled items? If it is, does it make sense to ask the model to approximate this random sample? And if it is not, then why is it used in the multinomial likelihood? 2) How does the model train? Does it train each phase at each iteration? Or it firstly trains the disentangle phase then the recommendation phase? \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "bVj4jNaUZ8", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "##########################################################################\n\nSummary:\n\nThis paper proposed a method (Untangle) which allows users to control over the recommendations with respect to specific item\nattributes. The proposed method leverages a supervised \f$\\beta$-VAE to disentangle item representations, and an additional step to generate recommendations. The proposed method is compared with some unsupervised baseline methods through experiments on two real-world datasets.\n\n##########################################################################\n\nStrength:\n\n- Interesting topic and practical in real-world application\n- Relatively extensive experiments are conducted\n\n##########################################################################\n\nWeakness:\n\n- Technical contribution/depth of this work is a bit limited\n- Execution of the experiments and evaluation may need some improvements\n- The overall presentation can be further improved\n\n##########################################################################\n\nDetailed Comments:\n\nThis work proposed an interesting angle to disentangle item representations and I personally like the pitch of recommendation interpretability and give control back to users. However the overall execution of this work may need some improvements, and I personally feel ICLR may not be the most appropriate venue for it, thus I tend to vote for a *rejection*.\n\n- With the current framing of the paper, I feel the technical depth of the proposed method (i.e. the two-phase supervised $\\beta$-VAE ) is a bit limited, especially considering the ICLR community. However I do like the interpretability and customization angle, as well as the efforts of exploring corresponding evaluation metrics. With that being said, a possibly better direction for improvement is re-framing and re-organizing the work to address the these pain points directly, rather than promote the algorithm standalone. The evaluation could also involve qualitative studies and get feedback from users directly. Regarding this I don't feel ICLR is an appropriate venue and recommendation/application-focused venue might be a better fit (e.g., RecSys/TheWebConf/WSDM/etc.).\n- Another major concern for the quantitive evaluation is, the proposed method is only compared to unsupervised baselines. One may argue that it may not be a fair comparison as the supervised reconstruction method leverages additional information, thus additional baselines with the same type of information may need to be considered.\n\n\nMinor Concerns:\n\n- There are some typos and grammar mistakes in the draft and the overall presentation is a bit mouthful (not notably affect my reading but worth double checking and improve in the next iteration)\n- The citation format in this draft looks informal (and different from other submissions in my queue) - please double check the latex template of the submission", "extra_scores": {}, "extra_text": {}}, {"review_id": "aca9ZmGE6uh", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The proposed approach leverages the beta-vae property that disentangles latent representation to do the critiquing operation on the recommendation system. A portion of latent representations dimensions of beta-VAE is further regularized by optimizing an additional objective that minimizes the loss of reconstructing some attributes of items, which enforce the particular latent dimension to represent a certain property of an item.\n\nIn the novelty perspective, the paper is based on disentangled beta-vae, which is well-established work. While applying the approach to the critiquing-based recommendation system is interesting, it does not hide the weakness of the novelty of this work (technically no much difference).\n\nIn terms of clearness, the paper is fairly written with reasonable description flow. However, some important information is missing. For example, in Equation 2, the author should emphasize the additional objective component by clearly state what $l$ function is. The objective function seems not correct as the expectation is on a single $c$ variable, but the loss is on a vector of $c$. I am not sure if this is a typo or intentional changes.\n\nThe author claims that the approach is distinguishable from the existing work [20, 21] since the existing work relies on data with attribute labels. However, when the paper introduces Equation 2, such a claim is already violated. Similarly, the attributes table (Table 1) also indicates such an effort of fetching attribute labels, which is no much different from Table 1 of [20]. I suggest the authors rephrase the related work section to clarify the similarity or difference. Meanwhile, I note that there is a paper that also makes a critiquing-based recommendation on VAE architecture. It should worth cite.\n\nThe experiment part does not fully reflect the paper's main claimed contribution in terms of critique. As the paper title stated, this paper leverage untangle to do the critiquing operation on recommender systems. While the author stated several existing works in the related work section, there is no comparison in the experiment but many the controllable attributes analysis (multiple figures deliver the exact same information.) Especially, such controllable attribute analysis has been well stated in the supervised beta VAE literature. The critiquing-based recommendation has a long history, and many classic approaches could be compared. \n\nRecommendation performance in Table 2 is not quite satisfactory as the proposed model's performance performs suboptimally on 2 out of 4 datasets. While the controller metric is far higher than the others, it is an unfair comparison as others baselines never optimize (or intended to optimize) such objective. \n\nIn terms of metric, the paper does not evaluate the proposed model's critiquing performance with proper metric (this is not a controller metric). MAP deduction (or so-called F-MAP) in [20] is a good example to evaluate.\n\n\nSome related papers:\n[1] Luo, Kai, et al. \"Deep Critiquing for VAE-Based Recommender Systems.\" Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Although borderline, all reviews are somewhat below the acceptance threshold. The main issue appears to be that the reviewers find the main claims unsupported by the experiments. Other complaints center around the presentation, which could be improved in a revision.
Decision: Reject