TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems

Recommender ( RS ) and Advertising/Marketing Systems ( AS ) play the key roles in E-commerce companies like Amazon and Alibaba . RS needs to generate thousands of item candidates for each user ( u2i ) , while AS needs to identify thousands or even millions of high-potential users for given items so that the merchant can advertise these items efficiently with limited budget ( i2u ) . This paper proposes an elegant bidirectional candidates generation framework that can serve both purposes all together . Specifically , our framework finds its practical applications on generating millions of users for a large number of items , and the marketing scenarios of small and medium enterprises ( SMEs ) with cloud-service . Besides , our framework is also superior in these aspects : i ) . We theoretically show that our modeling objective is equivalent to the modeling objectives of the SOTA algorithms if measured by the metrics of u2i and i2u . ii ) . Our framework can easily incorporate many DNN-architectures , and achieve SOTA results with the same architecture . iii ) . We empirically show that our framework can diversify the generated candidates , and ensure fast convergence . 1 INTRODUCTION . In the Internet era , E-commerce companies usually act as a platform to connect both users and merchants . They can collect huge amount of data of users ’ behavior on merchants ’ items , and utilize the data to serve them better , for example , to build better search/recommender/advertising systems . When users browse the E-commerce websites/apps , they usually want to buy some desired items , or find something interesting , and that ’ s what the search engine and recommender system ( RS ) are built for . Besides , the merchant usually wants to mine the potential users of its items , and then starts a campaign to prompt those users to buy the items . The advertising system has the functionality to help the merchant , and we may call it the potential users mining system ( PUMS ) . In an industrial large-scale RS , there are usually two stages , the candidates generation ( CG ) stage and the ranking stage ( Covington et al. , 2016 ) . CG plays a very import role as it sets the upper bound of the recommender . A single CG method usually lacks diversity , so the industrial CG system usually consists of different subsystems to generate candidate items , for example , i ) . hot items , ii ) . items similar to users ( Covington et al. , 2016 ; Li et al. , 2019 ) , iii ) . items similar to clicked items ( Linden et al. , 2003 ) , iv ) . items clicked by similar users ( Wang et al. , 2006 ) . The PUMS has been studied and applied in marketing community for many years ( Bult & Wansbeek , 1995 ; Kim et al. , 2005 ) , and recently it is also introduced to social network to attract potential customers of companies ( Pennacchiotti & Popescu , 2011 ; Pang et al. , 2013 ) . Similarly in E-commerce companies , it can be used to discover potential customers for merchants , brands and etc . Although it is widely used , people usually treat it as a simple binary classification problem , i.e. , to predict whether a given user is the potential user or not . Models like LR , SVM , XGBoost and etc . ( Hosmer Jr et al. , 2013 ; Chang & Lin , 2011 ; Chen & Guestrin , 2016 ) are usually employed . In this paper , we unify the two systems in our TotalRecall ( TR ) framework , and apply it in both large-scale PUMS and marketing scenarios of SMEs with the cloud-service . Our main contributions are listed below : • Bidirectional Candidates Generation : TR can be trained once and then used to generate candidate items ( u2i ) and users ( i2u ) all together without any additional operations . Theoretically we show that it is due to the modeling of joint probability of u and i . • Achieving SOTA results in both RS and PUMS : We compare to two different algorithms , i.e. , Matrix Factorization ( MF ) with Bernoulli distribution and sequential modeling and MF with Multinomial distribution , and achieve comparable or better results than previous SOTA . • Equivalence of the algorithms based on Bernoulli and Multinomial distributions : To the best of our knowledge , we are the first to show that modeling the user-item interaction with Bernoulli and Multinomial distributions are equivalent . • Fast Convergence , and Diversified Candidates : TR converges much faster compared to MF models , up to 16× times . Our experiments show that TR can increase the diversity of the generated candidates . 2 RELATED WORK . 2.1 RECOMMENDER SYSTEM ( RS ) . RS has been studied for many years , and in the early years , Collaborative Filtering ( CF ) and its variants ( Su & Khoshgoftaar , 2009 ) are widely adopted . Later its descendant MF ( Funk ) is proposed to solve the problem more elegantly with higher accuracy , and Probabilistic Matrix Factorization ( PMF ) ( Mnih & Salakhutdinov , 2008 ) builds a solid theoretic foundation for MF based on probability theory . Although MF performs very well in many datasets ( Rendle et al. , 2020 ) , it is usually not adopted in industry . The modern industrial RS can involve up to billions of users and hundreds of millions of items , and consist of multiple stages , e.g. , CG and ranking stages ( Covington et al. , 2016 ) . Various deep neural networks are adopted to tackle the complicated system , for example , in the CG stage , the problem is formulated as the multi-class classification , and users ’ and items ’ representations are detached with the two-tower architecture to balance the efficiency and accuracy in online serving ( Covington et al. , 2016 ; Li et al. , 2019 ; Cen et al. , 2020 ) . In the ranking stage , the problem is formulated as the binary classification , and various models has been developed : wide & deep model ( Cheng et al. , 2016 ) captures explicit and implicit feature crossing , Deep Interest Network ( Zhou et al. , 2018 ) and Deep User Perception Network ( Ni et al. , 2018 ) utilize attention mechanism ( Bahdanau et al . ( 2014 ) ) to capture users ’ short-term interests . 2.2 POTENTIAL USERS MINING SYSTEM ( PUMS ) . PUMS mines the potential users for given items . The ‘ item ’ could be anything that users can interact with , for example , an insurance product ( Kim & Street , 2004 ) , a company/business ( Pennacchiotti & Popescu , 2011 ; Pang et al. , 2013 ; Lo et al. , 2016 ) , a specific information ( e.g. , tweet ) in social medias ( Tang et al. , 2015 ; Gui et al. , 2019 ) and even another user ( Guy , 2018 ) . These problems are usually treated as binary classification and solved with tools like SVM , LR and etc . for each individual item independently ( Hosmer Jr et al. , 2013 ; Chang & Lin , 2011 ; Chen & Guestrin , 2016 ) . In the E-commerce company like Amazon , the ‘ item ’ could be a product , a brand , a product category , a merchant and etc , and the number of the ‘ items ’ range from thousands to hundreds of millions . It will be impractical to model each item individually , so we usually model the items all together using binary classification similar to Rendle et al . ( 2020 ) , which will be stated in detail later in Sec . 5 . 2.3 SAMPLED-SOFTMAX AND INFONCE LOSS . In the CG stage of RS , sampled-softmax ( SSM ) loss ( Jean et al. , 2015 ) is widely used with different background distributions , such as log-uniform distribution ( Powers , 1998 ) , empirical marginal distribution ( Yi et al. , 2019 ) and mixed distribution ( Yang et al. , 2020 ) . In contrastive learning , InfoNCE loss is proposed in Oord et al . ( 2018 ) to learn representations through self-supervision . Later it is widely adopted in CV ( Chen et al . ( 2020 ) ) and NLP ( Gao et al . ( 2021 ) ) and performs very well . InfoNCE and SSM losses are both trying to solve multi-class classification of very large vocabulary . They both use negative sampling and can be written in the same analytical form : l = 1 |D| ∑ ( x , y ) ∈D − log exp ( fϑ ( x , y ) − log pn ( y|x ) ) exp ( fϑ ( x , y ) − log pn ( y|x ) ) + ∑ y′∈Yx exp ( fϑ ( x , y ′ ) − log pn ( y′|x ) ) , ( 1 ) where ( x , y ) ∈ D is the training data , and usually x is the input features and y is the class , fϑ ( x , y ) is the output of the neural network parametrized by ϑ , and Yx contains hundreds or thousands of negative samples from background distribution pn ( y|x ) . The main differences are that the pn ( y|x ) in SSM can be arbitrarily chosen , while in InfoNCE it is the empirical marginal distribution p̂data ( y ) . Recently , InfoNCE loss is exploited in RS to optimize p ( y|x ) p ( y ) instead of p ( y|x ) for the debiasing purpose , and it can be easily implemented with in-batch negative sampling and features of y can be fully utilized ( Zhou et al. , 2021 ) . 3 METHOD . 3.1 PROBLEM DEFINITION . In E-commerce companies , when a user u clicked an item i at time t , a record ( u , i , t ) was logged as the implicit feedback . Given the raw logs { ( u , i , t ) } , there are usually two ways to process the data : one is to directly create a user-item interaction matrix Sui with the value su , i = 1 if the ( u , i ) pair appears in the log ( Su & Khoshgoftaar ( 2009 ) ) ; the other is to formulate the problem as next-item prediction , i.e. , from the raw log we create a dataset D = { ( xu , t , yu , t ) : t ∈ { 1 , 2 , ... , Tu } |u ∈ { 1 , 2 , ... , N } } , where xu , t = { yu,1 : ( t−1 ) } represents u ’ s clicks prior to t-th click yu , t and Tu is the number of clicks of u ( Covington et al . ( 2016 ) ) . We define the click sequence xu , t as the pseudo-user , so we can create a pseudo-user-item interaction matrix from D , and all possible click sequences form the pseudo-user set . Therefore , they both deal with the 2D interaction matrix Sui , irrespective of whether the u is a real user or pseudo-user or not . In the remaining of the paper , we will not distinguish between user and pseudo-user , and only focus on solving the Sui . Sui consists of all users as the row and all items as the column , and its entries are either 1 or unknown . Let ’ s assume all users form the set U = { u1 , u2 , ... , uM } , and all items form the set I = { i1 , i2 , ... , iK } . Given a user u ∈ U , we will generate candidate items from the item pool I ( RS , u2i ) , while given an item i ∈ I , we shall find out the potential users out of all users U ( PUMS , i2u ) . The candidates generation of RS and PUMS are of the opposite direction , and traditionally they are solved separately . But essentially they are both trying to estimate some kind of probabilities of the unknown entries in Sui , so we propose to model them jointly .

Summary: This work proposes a bidirectional candidate generation framework for both recommender system (which provides items for a user) and advertising system (which provides users for an item). Specifically, the authors conduct a Bi-InfoNCE loss on the classical two-tower architecture to jointly learn both user and item representations. The Bi-InfoNCE loss considers each (u, i) pair as a positive instance, and selects both users and items as negative instances in InfoNCE. In experiments, the authors find that the proposed TR model outperforms MF in both recommender system and advertising system datasets.