Getting Less Top-k accuracy comparing to other Open source Recommendation systems

We have done a comparison of TFRS with Lightfm (a open source library which is for recommendation system) and Result shows that Lightfm performs better than TFRS. Here we use the dataset which is available in amazon blog for evaluating recommendation system. The result is showed as follows. Is there any way we can improve the accuracy further?

Here i am attaching Train- Test Top-k visualisations

sample code

class UserModel(tf.keras.Model):

def __init__(self):
    super().__init__()

    emb_dim = 32    
    self.user_id_embedding = tf.keras.Sequential([
        tf.keras.layers.experimental.preprocessing.StringLookup(
            vocabulary=USER_ID_unique, mask_token=None),
        tf.keras.layers.Embedding(len(USER_ID_unique) + 1, emb_dim),
    ])
        
    self.cabin_type_embedding = tf.keras.Sequential([
        tf.keras.layers.experimental.preprocessing.StringLookup(
            vocabulary= CABIN_TYPE_unique, mask_token=None),  
        tf.keras.layers.Embedding(len(CABIN_TYPE_unique) + 1, emb_dim),
    ])

    self.user_residence_embedding = tf.keras.Sequential([
        tf.keras.layers.experimental.preprocessing.StringLookup(
            vocabulary=USER_RESIDENCE_unique, mask_token=None),
        tf.keras.layers.Embedding(len(USER_RESIDENCE_unique) + 1, emb_dim),
    ])
    

def call(self, user_interation_data):
    return tf.concat([                          
        self.user_id_embedding(user_interation_data["USER_ID"]), 
        self.cabin_type_embedding(user_interation_data["CABIN_TYPE"]), 
        self.user_residence_embedding(user_interation_data["USER_RESIDENCE"]),
    ], axis=1)

class ItemModel(tf.keras.Model):

def __init__(self):
    super().__init__()

    

    self.item_embedding = tf.keras.Sequential([
        tf.keras.layers.experimental.preprocessing.StringLookup(
            vocabulary=item_unique, mask_token=None),
        tf.keras.layers.Embedding(len(item_unique) + 1, 32),
    ])


def call(self, user_interation_data):

    return tf.concat([
        self.item_embedding(user_interation_data["ITEM_ID"])
        
        ], axis=1)

class TRFSRetrievalModel(tfrs.models.Model):

def __init__(self, UserModel,ItemModel, item_ds ):
    super().__init__()

    self.query_model = tf.keras.Sequential([#,UserModel()
      UserModel(),
      tf.keras.layers.Dense(32 , kernel_initializer= tf.keras.initializers.RandomNormal(seed=99)),   
    ])
    

    self.candidate_model = tf.keras.Sequential([
      ItemModel(),
      tf.keras.layers.Dense(32, kernel_initializer= tf.keras.initializers.RandomNormal(seed=1))
    ]) 
    
    
    self.task = tfrs.tasks.Retrieval(
        metrics=tfrs.metrics.FactorizedTopK(
        item_ds.map(self.candidate_model),
            ks= (3, 5, 10,15, 25))
    )
    
def compute_loss(self, features, training= True):

    item_features = {"ITEM_ID":features.pop("ITEM_ID") }
    query_embeddings = self.query_model(features)
    item_embeddings = self.candidate_model(item_features)

    return self.task(query_embeddings, 
    item_embeddings, 
    compute_metrics=True
    )