1. To run the HEM model in ./HEM/ and the python scripts in ./scripts/, python 2.7+ and Tensorflow v1.0+ are needed. (In the paper, we used python 2.7.12 and Tensorflow v1.4.0)
2. To run the jar package in ./jar/, JDK 1.7 is needed
3. To compile the java code in ./java/, galago from lemur project (https://sourceforge.net/p/lemur/wiki/Galago%20Installation/) is needed. 

1. Download Amazon review datasets from http://jmcauley.ucsd.edu/data/amazon/ (e.g. In our paper, we used 5-core data)
2. Stem and remove stop words from the Amazon review datasets if needed (e.g. In our paper, we stem the field of “reviewText” and “summary” without stop words removal)
    1. java -Xmx4g -jar ./jar/AmazonReviewData_preprocess.jar <jsonConfigFile> <review_file> <output_review_file>
        1. <jsonConfigFile>: A json file that specify the file path of stop words list. An example can be found in the root directory. Enter “false” if don’t want to remove stop words. 
        2. <review_file>: the path for the original Amazon review data
        3. <output_review_file>: the output path for processed Amazon review data
3. Index datasets
    1. python ./scripts/index_and_filter_review_file.py <review_file> <indexed_data_dir> <min_count>
        1. <review_file>: the file path for the Amazon review data
        2. <indexed_data_dir>: output directory for indexed data
        3. <min_count>: the minimum count for terms. If a term appears less then <min_count> times in the data, it will be ignored.
4. Extract queries and Split train/test
    1. Download the meta data from http://jmcauley.ucsd.edu/data/amazon/ 
    2. Match the meta data with the indexed data:
        1. java -Xmx16G -jar ./jar/AmazonMetaData_matching.jar <jsonConfigFile> <meta_data_file> <indexed_data_dir>
            1. <jsonConfigFile> : A json file that specify the file path of stop words list. An example can be found in the root directory. Enter “false” if don’t want to remove stop words. 
            2. <meta_data_file>:  the path for the meta data
            3. <indexed_data_dir>: the directory for indexed data
    3. Split datasets for training and test
        1. python ./scripts/split_train_test_data.py <indexed_data_dir> <review_sample_rate> <query_sample_rate>
        2. <indexed_data_dir>: the directory for indexed data
        3. <review_sample_rate>: the proportion of reviews used in test for each user (e.g. in our paper, we used 0.3).
        4. <query_sample_rate>: the proportion of queries used in test (e.g. in our paper, we used 0.3).

1. python ./HEM/main.py --<parameter_name> <parameter_value> --<parameter_name> <parameter_value> … 
    1. learning_rate:  The learning rate in training. Default 0.5.
    2. learning_rate_decay_factor: Learning rate decays by this much whenever the loss is higher than three previous loss. Default 0.90
    3. max_gradient_norm: Clip gradients to this norm. Default 5.0
    4. subsampling_rate: The rate to subsampling. Default 1e-4. 
    5. L2_lambda: The lambda for L2 regularization. Default 0.0
    6. query_weight: The weight for queries in the joint model of queries and users. Default 0.5
    7. batch_size: Batch size used in training. Default 64
    8. data_dir: Data directory, which should be the <indexed_data_dir>
    9. input_train_dir: The directory of training and testing data, which usually is <data_dir>/query_split/
    10. train_dir: Model directory & output directory
    11. similarity_func: The function to compute the ranking score for an item with the joint model of query and user embeddings. Default “product”.
        1. “product”: the dot product of two vectors.
        2. “cosine”: the cosine similarity of two vectors.
        3. “bias_product”: the dot product plus a item-specific bias
    12. net_struct:  Network structure parameters. Different parameters are separated by “_” (e.g. ). Default “simplified_fs”
        1. “LSE”: the latent space entity model proposed by Gysel et al. [1]
        2. “simplified”: simplified embedding-based language models without modeling for each review [2]
        3. “pv”: embedding-based language models with review modeling using paragraph vector model. [3]
        4. “hdc”: regularized embedding-based language models with word context. [4]
        5. “mean”: average word embeddings for query embeddings [5]
        6. “fs”: average word embeddings with non-linear projection for query embeddings [1]
        7. “RNN”: recurrent neural network encoder for query embeddings
    13. embed_size: Size of each embedding. Default 100.
    14. window_size: Size of context window for hdc model. Default 5.
    15. max_train_epoch: Limit on the epochs of training (0: no limit). Default 5.
    16. steps_per_checkpoint: How many training steps to do per checkpoint. Default 200
    17. seconds_per_checkpoint: How many seconds to wait before storing embeddings. Default 3600
    18. negative_sample: How many samples to generate for negative sampling. Default 5.
    19. decode: Set to “False" for training and “True" for testing. Default “False"
    20. test_mode: Test modes. Default “product_scores"
        1. “product_scores”: output ranking results and ranking scores; 
        2. “output_embedding": output embedding representations for users, items and words.
    21. rank_cutoff: Rank cutoff for output rank lists. Default 100.
2. Evaluation
    1. After training with "--decode False”, generate test rank lists with "--decode True”.
    2. TREC format rank lists for test data will be stored in <train_dir> with name “test.<similarity_func>.ranklist”
    3. Evaluate test rank lists with ground truth <input_train_dir>/test.qrels using trec_eval or galago eval tool.

min_count —-> 5 
learning_rate —-> 0.5
steps_per_checkpoint -—> 400
max_train_epoch -—> 20
embed_size  —-> 400 (for Electronic), 100 (for Kindle), 300 (for CDs), 100 (for Cell Phone)
subsampling_rate  --> 1e-4 (for Electronic, Kindle, CDs), 0.0 (for Cell Phone)
L2_lambda  —-> 0.005
batch_size  —-> 64
window_size  —-> 3
negative_sample  -—> 5
rank_cutoff  —-> 100
similarity_func  --> 'cosine', ‘product' or 'bias_product' 
net_struct  --> ‘simplified_fs’ 

[1] Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016. Learninglatent vector spaces for product search. In Proceedings of the 25th ACM CIKM.
[2] Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, W. Bruce Croft. 2017. Learning a Hierarchical Embedding Model for Personalized ProductSearch. In Proceedings of SIGIR ’17
[3] Quoc V Le and Tomas Mikolov. 2014. Distributed Representations of Sentencesand Documents.. In ICML
[4] Sun, Fei, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. 2015 "Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations." In ACL 
[5] Ivan Vulic ́ and Marie-Francine Moens. 2015. Monolingual and cross-lingual in-formation retrieval models based on (bilingual) word embeddings. In Proceedingsof the 38th ACM SIGIR